diff --git a/doc/modelchain_example_notebook.ipynb b/doc/modelchain_example_notebook.ipynb
index 6086524a..46f982b5 100644
--- a/doc/modelchain_example_notebook.ipynb
+++ b/doc/modelchain_example_notebook.ipynb
@@ -152,9 +152,9 @@
    "source": [
     "To initialize a specific turbine you need a dictionary that contains the basic parameters. A turbine is defined by its nominal power, hub height, rotor diameter, and power or power coefficient curve.\n",
     "\n",
-    "There are two ways to initialize a WindTurbine object in the windpowerlib. You can either specify your own turbine, as done below for 'myTurbine', or fetch power and/or power coefficient curve data from data files provided by the windpowerlib, as done for the 'enerconE126'.\n",
+    "There are three ways to initialize a WindTurbine object in the windpowerlib. You can either specify your own turbine, as done below for 'myTurbine', or fetch power and/or power coefficient curve data from data files provided by the windpowerlib, as done for the 'enerconE126', or provide your turbine data in csv files as done for the 'example_turbine' with an example file.\n",
     "\n",
-    "You can execute the following to get a list of all wind turbines for which power or power coefficient curves are provided."
+    "You can execute the following to get a table of all wind turbines for which power and/or power coefficient curves are provided."
    ]
   },
   {
@@ -166,40 +166,58 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "            turbine_id    p_nom\n",
-      "52   ENERCON E 70 2300  2300000\n",
-      "64  ENERCON E 101 3000  3000000\n",
-      "65  ENERCON E 126 7500  7500000\n",
-      "66  ENERCON E 115 2500  2500000\n",
-      "67    ENERCON E 48 800   800000\n",
-      "68   ENERCON E 82 2000  2000000\n",
-      "69    ENERCON E 53 800   800000\n",
-      "70   ENERCON E 58 1000  1000000\n",
-      "71   ENERCON E 70 2000  2000000\n",
-      "72   ENERCON E 82 2300  2300000\n",
-      "73   ENERCON E 82 3000  3000000\n",
-      "74   ENERCON E 92 2300  2300000\n",
-      "75  ENERCON E 112 4500  4500000\n"
+      "   manufacturer turbine_type  has_power_curve  has_cp_curve\n",
+      "1       Enercon   E-101/3050             True          True\n",
+      "2       Enercon   E-101/3500             True          True\n",
+      "3       Enercon   E-115/3000             True          True\n",
+      "4       Enercon   E-115/3200             True          True\n",
+      "5       Enercon   E-126/4200             True          True\n",
+      "6       Enercon   E-141/4200             True          True\n",
+      "7       Enercon     E-53/800             True          True\n",
+      "8       Enercon    E-70/2000             True          True\n",
+      "9       Enercon    E-70/2300             True          True\n",
+      "10      Enercon    E-82/2000             True          True\n",
+      "11      Enercon    E-82/2300             True          True\n",
+      "12      Enercon    E-82/2350             True          True\n",
+      "13      Enercon    E-82/3000             True          True\n",
+      "14      Enercon    E-92/2350             True          True\n",
+      "15      Enercon      E48/800             True          True\n"
      ]
     }
    ],
    "source": [
     "# get power curves\n",
-    "# get names of wind turbines for which power curves are provided (default)\n",
+    "# get names of wind turbines for which power curves and/or are provided\n",
     "# set print_out=True to see the list of all available wind turbines\n",
-    "wt.get_turbine_types(print_out=False)\n",
+    "df = wt.get_turbine_types(print_out=False)\n",
     "\n",
-    "# get power coefficient curves\n",
-    "# write names of wind turbines for which power coefficient curves are provided\n",
-    "# to 'turbines' DataFrame\n",
-    "turbines = wt.get_turbine_types(filename='power_coefficient_curves.csv', print_out=False)\n",
-    "# find all Enercons in 'turbines' DataFrame\n",
-    "print(turbines[turbines[\"turbine_id\"].str.contains(\"ENERCON\")])"
+    "# find all Enercons\n",
+    "print(df[df[\"manufacturer\"].str.contains(\"Enercon\")])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  manufacturer turbine_type  has_power_curve  has_cp_curve\n",
+      "1      Enercon   E-101/3050             True          True\n",
+      "2      Enercon   E-101/3500             True          True\n"
+     ]
+    }
+   ],
+   "source": [
+    "# find all Enercon 101 turbines\n",
+    "print(df[df[\"turbine_type\"].str.contains(\"E-101\")])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -229,22 +247,42 @@
     "# if you want to use the power coefficient curve change the value of\n",
     "# 'fetch_curve' to 'power_coefficient_curve'\n",
     "enerconE126 = {\n",
-    "    'name': 'ENERCON E 126 7500',  # turbine name as in register\n",
-    "    'hub_height': 135,  # in m\n",
-    "    'rotor_diameter': 127,  # in m\n",
-    "    'fetch_curve': 'power_curve'  # fetch power curve\n",
-    "}\n",
+    "        'name': 'E-126/4200',  # turbine type as in register #\n",
+    "        'hub_height': 135,  # in m\n",
+    "        'rotor_diameter': 127,  # in m\n",
+    "        'fetch_curve': 'power_curve',  # fetch power curve #\n",
+    "        'data_source': 'oedb'  # data source oedb or name of csv file\n",
+    "    }\n",
     "# initialize WindTurbine object\n",
     "e126 = WindTurbine(**enerconE126)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# specification of wind turbine where power coefficient curve is provided\n",
+    "# by a csv file\n",
+    "dummyTurbine = {\n",
+    "    'name': 'DUMMY 1',  # turbine type as in file #\n",
+    "    'hub_height': 100,  # in m\n",
+    "    'rotor_diameter': 70,  # in m\n",
+    "    'fetch_curve': 'power_coefficient_curve',  # fetch cp curve #\n",
+    "    'data_source': 'example_power_coefficient_curves.csv'  # data source\n",
+    "}\n",
+    "# initialize WindTurbine object\n",
+    "dummy_turbine = WindTurbine(**dummyTurbine)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Use the ModelChain to calculate turbine power output\n",
     "\n",
-    "The ModelChain is a class that provides all necessary steps to calculate the power output of a wind turbine. If you use the 'run_model' method first the wind speed and density (if necessary) at hub height are calculated and then used to calculate the power output. You can either use the default methods for the calculation steps, as done for 'my_turbine', or choose different methods, as done for the 'e126'."
+    "The ModelChain is a class that provides all necessary steps to calculate the power output of a wind turbine. If you use the 'run_model' method first the wind speed and density (if necessary) at hub height are calculated and then used to calculate the power output. You can either use the default methods for the calculation steps, as done for 'my_turbine', or choose different methods, as done for the 'e126'. Of course, you can also use the default methods while only changing one or two of them, as done for 'dummy_turbine'."
    ]
   },
   {
@@ -311,6 +349,31 @@
     "e126.power_output = mc_e126.power_output"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:root:Calculating wind speed using logarithmic wind profile.\n",
+      "DEBUG:root:Calculating temperature using temperature gradient.\n",
+      "DEBUG:root:Calculating density using barometric height equation.\n",
+      "DEBUG:root:Calculating power output using power coefficient curve.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# power output calculation for example_turbine\n",
+    "# own specification for 'power_output_model'\n",
+    "mc_example_turbine = ModelChain(\n",
+    "    dummy_turbine,\n",
+    "    power_output_model='power_coefficient_curve').run_model(weather)\n",
+    "dummy_turbine.power_output = mc_example_turbine.power_output"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -329,6 +392,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "DEBUG:matplotlib:CACHEDIR=/home/sabine/.cache/matplotlib\n",
+      "DEBUG:matplotlib.font_manager:Using fontManager instance from /home/sabine/.cache/matplotlib/fontlist-v300.json\n",
+      "DEBUG:matplotlib.pyplot:Loaded backend module://ipykernel.pylab.backend_inline version unknown.\n",
+      "DEBUG:matplotlib.pyplot:Loaded backend module://ipykernel.pylab.backend_inline version unknown.\n",
       "DEBUG:matplotlib.pyplot:Loaded backend module://ipykernel.pylab.backend_inline version unknown.\n"
      ]
     }
@@ -361,7 +428,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -377,6 +444,7 @@
     "if plt:\n",
     "    e126.power_output.plot(legend=True, label='Enercon E126')\n",
     "    my_turbine.power_output.plot(legend=True, label='myTurbine')\n",
+    "    dummy_turbine.power_output.plot(legend=True, label='dummyTurbine')\n",
     "    plt.show()"
    ]
   },
@@ -398,7 +466,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
diff --git a/doc/turbine_cluster_modelchain_example_notebook.ipynb b/doc/turbine_cluster_modelchain_example_notebook.ipynb
index b031d131..a158221d 100644
--- a/doc/turbine_cluster_modelchain_example_notebook.ipynb
+++ b/doc/turbine_cluster_modelchain_example_notebook.ipynb
@@ -51,7 +51,20 @@
       "height                            10       80          2       10       0 \n",
       "2010-01-01 00:00:00+01:00    5.32697  7.80697      267.60  267.57  98405.7\n",
       "2010-01-01 01:00:00+01:00    5.46199  7.86199      267.60  267.55  98382.7\n",
-      "2010-01-01 02:00:00+01:00    5.67899  8.59899      267.61  267.54  98362.9\n",
+      "2010-01-01 02:00:00+01:00    5.67899  8.59899      267.61  267.54  98362.9\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Data base connection successful.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\n",
       "nominal power of my_turbine: 3000000.0\n"
      ]
@@ -63,7 +76,7 @@
     "print(weather[['wind_speed', 'temperature', 'pressure']][0:3])\n",
     "\n",
     "# Initialize wind turbines\n",
-    "my_turbine, e126 = mc_e.initialize_wind_turbines()\n",
+    "my_turbine, e126, dummy_turbine = mc_e.initialize_wind_turbines()\n",
     "print()\n",
     "print('nominal power of my_turbine: {}'.format(my_turbine.nominal_power))"
    ]
diff --git a/doc/whatsnew/v0-1-0.txt b/doc/whatsnew/v0-1-0.txt
index f74c40f8..3f02b347 100644
--- a/doc/whatsnew/v0-1-0.txt
+++ b/doc/whatsnew/v0-1-0.txt
@@ -17,6 +17,7 @@ New functions
  * logarithmic interpolation/extrapolation for wind speed time series
  * gauss distribution
  * estimation of turbulence intensity by roughness length
+ * retrieve power curves from Open Energy Database
 
 
 Testing
diff --git a/example/modelchain_example.ipynb b/example/modelchain_example.ipynb
index 6086524a..46f982b5 100644
--- a/example/modelchain_example.ipynb
+++ b/example/modelchain_example.ipynb
@@ -152,9 +152,9 @@
    "source": [
     "To initialize a specific turbine you need a dictionary that contains the basic parameters. A turbine is defined by its nominal power, hub height, rotor diameter, and power or power coefficient curve.\n",
     "\n",
-    "There are two ways to initialize a WindTurbine object in the windpowerlib. You can either specify your own turbine, as done below for 'myTurbine', or fetch power and/or power coefficient curve data from data files provided by the windpowerlib, as done for the 'enerconE126'.\n",
+    "There are three ways to initialize a WindTurbine object in the windpowerlib. You can either specify your own turbine, as done below for 'myTurbine', or fetch power and/or power coefficient curve data from data files provided by the windpowerlib, as done for the 'enerconE126', or provide your turbine data in csv files as done for the 'example_turbine' with an example file.\n",
     "\n",
-    "You can execute the following to get a list of all wind turbines for which power or power coefficient curves are provided."
+    "You can execute the following to get a table of all wind turbines for which power and/or power coefficient curves are provided."
    ]
   },
   {
@@ -166,40 +166,58 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "            turbine_id    p_nom\n",
-      "52   ENERCON E 70 2300  2300000\n",
-      "64  ENERCON E 101 3000  3000000\n",
-      "65  ENERCON E 126 7500  7500000\n",
-      "66  ENERCON E 115 2500  2500000\n",
-      "67    ENERCON E 48 800   800000\n",
-      "68   ENERCON E 82 2000  2000000\n",
-      "69    ENERCON E 53 800   800000\n",
-      "70   ENERCON E 58 1000  1000000\n",
-      "71   ENERCON E 70 2000  2000000\n",
-      "72   ENERCON E 82 2300  2300000\n",
-      "73   ENERCON E 82 3000  3000000\n",
-      "74   ENERCON E 92 2300  2300000\n",
-      "75  ENERCON E 112 4500  4500000\n"
+      "   manufacturer turbine_type  has_power_curve  has_cp_curve\n",
+      "1       Enercon   E-101/3050             True          True\n",
+      "2       Enercon   E-101/3500             True          True\n",
+      "3       Enercon   E-115/3000             True          True\n",
+      "4       Enercon   E-115/3200             True          True\n",
+      "5       Enercon   E-126/4200             True          True\n",
+      "6       Enercon   E-141/4200             True          True\n",
+      "7       Enercon     E-53/800             True          True\n",
+      "8       Enercon    E-70/2000             True          True\n",
+      "9       Enercon    E-70/2300             True          True\n",
+      "10      Enercon    E-82/2000             True          True\n",
+      "11      Enercon    E-82/2300             True          True\n",
+      "12      Enercon    E-82/2350             True          True\n",
+      "13      Enercon    E-82/3000             True          True\n",
+      "14      Enercon    E-92/2350             True          True\n",
+      "15      Enercon      E48/800             True          True\n"
      ]
     }
    ],
    "source": [
     "# get power curves\n",
-    "# get names of wind turbines for which power curves are provided (default)\n",
+    "# get names of wind turbines for which power curves and/or are provided\n",
     "# set print_out=True to see the list of all available wind turbines\n",
-    "wt.get_turbine_types(print_out=False)\n",
+    "df = wt.get_turbine_types(print_out=False)\n",
     "\n",
-    "# get power coefficient curves\n",
-    "# write names of wind turbines for which power coefficient curves are provided\n",
-    "# to 'turbines' DataFrame\n",
-    "turbines = wt.get_turbine_types(filename='power_coefficient_curves.csv', print_out=False)\n",
-    "# find all Enercons in 'turbines' DataFrame\n",
-    "print(turbines[turbines[\"turbine_id\"].str.contains(\"ENERCON\")])"
+    "# find all Enercons\n",
+    "print(df[df[\"manufacturer\"].str.contains(\"Enercon\")])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  manufacturer turbine_type  has_power_curve  has_cp_curve\n",
+      "1      Enercon   E-101/3050             True          True\n",
+      "2      Enercon   E-101/3500             True          True\n"
+     ]
+    }
+   ],
+   "source": [
+    "# find all Enercon 101 turbines\n",
+    "print(df[df[\"turbine_type\"].str.contains(\"E-101\")])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -229,22 +247,42 @@
     "# if you want to use the power coefficient curve change the value of\n",
     "# 'fetch_curve' to 'power_coefficient_curve'\n",
     "enerconE126 = {\n",
-    "    'name': 'ENERCON E 126 7500',  # turbine name as in register\n",
-    "    'hub_height': 135,  # in m\n",
-    "    'rotor_diameter': 127,  # in m\n",
-    "    'fetch_curve': 'power_curve'  # fetch power curve\n",
-    "}\n",
+    "        'name': 'E-126/4200',  # turbine type as in register #\n",
+    "        'hub_height': 135,  # in m\n",
+    "        'rotor_diameter': 127,  # in m\n",
+    "        'fetch_curve': 'power_curve',  # fetch power curve #\n",
+    "        'data_source': 'oedb'  # data source oedb or name of csv file\n",
+    "    }\n",
     "# initialize WindTurbine object\n",
     "e126 = WindTurbine(**enerconE126)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# specification of wind turbine where power coefficient curve is provided\n",
+    "# by a csv file\n",
+    "dummyTurbine = {\n",
+    "    'name': 'DUMMY 1',  # turbine type as in file #\n",
+    "    'hub_height': 100,  # in m\n",
+    "    'rotor_diameter': 70,  # in m\n",
+    "    'fetch_curve': 'power_coefficient_curve',  # fetch cp curve #\n",
+    "    'data_source': 'example_power_coefficient_curves.csv'  # data source\n",
+    "}\n",
+    "# initialize WindTurbine object\n",
+    "dummy_turbine = WindTurbine(**dummyTurbine)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Use the ModelChain to calculate turbine power output\n",
     "\n",
-    "The ModelChain is a class that provides all necessary steps to calculate the power output of a wind turbine. If you use the 'run_model' method first the wind speed and density (if necessary) at hub height are calculated and then used to calculate the power output. You can either use the default methods for the calculation steps, as done for 'my_turbine', or choose different methods, as done for the 'e126'."
+    "The ModelChain is a class that provides all necessary steps to calculate the power output of a wind turbine. If you use the 'run_model' method first the wind speed and density (if necessary) at hub height are calculated and then used to calculate the power output. You can either use the default methods for the calculation steps, as done for 'my_turbine', or choose different methods, as done for the 'e126'. Of course, you can also use the default methods while only changing one or two of them, as done for 'dummy_turbine'."
    ]
   },
   {
@@ -311,6 +349,31 @@
     "e126.power_output = mc_e126.power_output"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:root:Calculating wind speed using logarithmic wind profile.\n",
+      "DEBUG:root:Calculating temperature using temperature gradient.\n",
+      "DEBUG:root:Calculating density using barometric height equation.\n",
+      "DEBUG:root:Calculating power output using power coefficient curve.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# power output calculation for example_turbine\n",
+    "# own specification for 'power_output_model'\n",
+    "mc_example_turbine = ModelChain(\n",
+    "    dummy_turbine,\n",
+    "    power_output_model='power_coefficient_curve').run_model(weather)\n",
+    "dummy_turbine.power_output = mc_example_turbine.power_output"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -329,6 +392,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "DEBUG:matplotlib:CACHEDIR=/home/sabine/.cache/matplotlib\n",
+      "DEBUG:matplotlib.font_manager:Using fontManager instance from /home/sabine/.cache/matplotlib/fontlist-v300.json\n",
+      "DEBUG:matplotlib.pyplot:Loaded backend module://ipykernel.pylab.backend_inline version unknown.\n",
+      "DEBUG:matplotlib.pyplot:Loaded backend module://ipykernel.pylab.backend_inline version unknown.\n",
       "DEBUG:matplotlib.pyplot:Loaded backend module://ipykernel.pylab.backend_inline version unknown.\n"
      ]
     }
@@ -361,7 +428,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -377,6 +444,7 @@
     "if plt:\n",
     "    e126.power_output.plot(legend=True, label='Enercon E126')\n",
     "    my_turbine.power_output.plot(legend=True, label='myTurbine')\n",
+    "    dummy_turbine.power_output.plot(legend=True, label='dummyTurbine')\n",
     "    plt.show()"
    ]
   },
@@ -398,7 +466,7 @@
     },
     {
      "data": {
-      "image/png": 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mPY2n7TUrMz8F2CqBWyZmZeanAFslcDAxKzM/Bdgqgbu5zLYDzU8BPmPsCOYsXkWjB+Gth/GzuczMrFV+NpeZmXUbBxMzM8vNwcTMzHJzMDEzs9wcTMzMLLeigomkwZJ+Iuk5ScskHS5pZ0kLJK1If4ekvJI0Q1K9pCclHVpwnKkp/wpJUwvSD5P0VNpnRpoLno6UYWZm3a/Ylsn3gfsi4qPAx4FlwKXAwogYDSxM6wATgdHpNQ24DrLAAFwBjAPGAlc0B4eU55yC/Sak9JLKMDOz8mg3mEj6EHAUcCNARLwVEa8Dk4BZKdss4KS0PAm4JTKPAoMl7QGcACyIiA0R8RqwAJiQtg2KiEcju+nllhbHKqUMMzMrg2JaJqOARuDfJT0m6ceSdgR2j4i1Kc/LwO5peSiwumD/hpTWVnrDNtLpQBlmZlYGxQSTKuBQ4LqIOAT4I+91NwGQWhRdeit9R8qQNE1SnaS6xsbGLqqZmZkVE0wagIaIWJTWf0IWXF5p7lpKf9el7WuA4QX7D0tpbaUP20Y6HSjjfSJiZkTURkRtTU1NEadqZmYd0W4wiYiXgdWS9ktJxwHPAvOA5iuypgJ3p+V5wJR0xdV44I3UVTUfOF7SkDTwfjwwP23bKGl8uoprSotjlVKGmZmVQbFPDf474DZJOwAvAF8iC0R3SDobeAk4NeW9B/gsUA+8mfISERskfQtYkvJ9MyI2pOXzgZuBAcC96QVwVSllmG1v1m3czAVzH+MHZxziR8pbRfNTg8260OV3PcVti1cxeewIpn/h4HJXx6xkxT412POZmHUBT8VrvY0fp2LWBTwVr/U2DiZmXcBT8Vpv424usy7iqXitN/EAvJmZtcrT9pqZWbdxMDEzs9wcTMzMLDcHEzMzy83BxMzMcnMwMTOz3BxMzMwsNwcTMzPLzcHEzMxyczAxM7PcHEzMzCw3BxMzM8utqGAiaaWkpyQ9Lqkupe0saYGkFenvkJQuSTMk1Ut6UtKhBceZmvKvkDS1IP2wdPz6tK86WoaZmXW/Ulomx0TEmIKnR14KLIyI0cDCtA4wERidXtOA6yALDMAVwDhgLHBFc3BIec4p2G9CR8owM7PyyNPNNQmYlZZnAScVpN8SmUeBwZL2AE4AFkTEhoh4DVgATEjbBkXEo5E9D/+WFscqpQwzMyuDYoNJAL+UtFTStJS2e0SsTcsvA7un5aHA6oJ9G1JaW+kN20jvSBlmZlYGxc60+MmIWCNpN2CBpOcKN0ZESOrSWbY6UkYKfNMARowY0SX1MjOzIlsmEbEm/V0H3EU25vFKc9dS+rsuZV8DDC/YfVhKayt92DbS6UAZLes9MyJqI6K2pqammFM1a9O6jZs59YZHWOcpeM3ep91gImlHSQObl4HjgaeBeUDzFVlTgbvT8jxgSrriajzwRuqqmg8cL2lIGng/Hpiftm2UND5dxTWlxbFKKcOsS81YuIIlKzcw41cryl0Vs+1KMd1cuwN3pat1q4A5EXGfpCXAHZLOBl4CTk357wE+C9QDbwJfAoiIDZK+BSxJ+b4ZERvS8vnAzcAA4N70AriqlDLMusp+l99L05at767PXrSK2YtW0b+qD8unTyxjzcy2D8ouoKp8tbW1UVdXV+5qWA+1buNmpt+zjF8+8zKb395Kdb8+nHDgh/n65/Znt4HV5a6eWZeRtLTglpBW+Q54syLsNqiagf2raNqylf5VfWjaspWB/ascSMySYq/mMuv1Xt3UxORxe3HG2BHMWbyKRg/Cm73L3VxmZtYqd3OZmVm3cTAxM7PcHEzMzCw3BxMzM8vNwcTMzHJzMDEzs9wcTMzMLDcHEzMzy83BxMzMcnMwMTOz3BxMzMwsNwcTMzPLzcHEzMxyczAxM7Pcig4mkvpKekzSL9L6KEmLJNVL+g9JO6T0/mm9Pm0fWXCMy1L6ckknFKRPSGn1ki4tSC+5DDMz636ltEy+AiwrWP8ucHVEfAR4DTg7pZ8NvJbSr075kHQAcBpwIDAB+GEKUH2Ba4GJwAHA6SlvyWWYmVl5FBVMJA0DPgf8OK0LOBb4ScoyCzgpLU9K66Ttx6X8k4DbI6IpIl4E6oGx6VUfES9ExFvA7cCkDpZhZmZlUGzL5BrgYmBrWt8FeD0itqT1BmBoWh4KrAZI299I+d9Nb7FPa+kdKcPMzMqg3WAi6S+AdRGxtBvq06kkTZNUJ6musbGx3NUxM6tYxbRMjgBOlLSSrAvqWOD7wGBJVSnPMGBNWl4DDAdI2z8ErC9Mb7FPa+nrO1DG+0TEzIiojYjampqaIk7VzMw6ot1gEhGXRcSwiBhJNoB+f0RMBn4NnJyyTQXuTsvz0jpp+/0RESn9tHQl1ihgNLAYWAKMTldu7ZDKmJf2KbUMMzMrgzz3mVwCXCipnmy84saUfiOwS0q/ELgUICKeAe4AngXuA74cEe+kMY8LgPlkV4vdkfKWXIZZqdZt3MypNzzCuj9sLndVzHo09ZYf9LW1tVFXV1fuath25vK7nuK2xauYPHYE079wcLmrY7bdkbQ0Imrby1fVXgazSrTf5ffStGXru+uzF61i9qJV9K/qw/LpE8tYM7OeyY9TsV7poYuP4cQxe1LdL/svUN2vD5PG7MlDlxxT5pqZ9UwOJtYr7TaomoH9q2jaspX+VX1o2rKVgf2r2G1gdbmrZtYjuZvLeq1XNzUxedxenDF2BHMWr6LRg/BmHeYBeDMza1WxA/Du5jIzs9wcTMzMLDcHEzMzy83BxMzMcnMwMTOz3BxMzMwsNwcTMzPLzcHEzMxyczAxM7PcHEzMzCw3BxMzM8vNwcTMzHJzMDEzs9zaDSaSqiUtlvSEpGckfSOlj5K0SFK9pP+QtENK75/W69P2kQXHuiylL5d0QkH6hJRWL+nSgvSSyzAzs+5XTMukCTg2Ij4OjAEmSBoPfBe4OiI+ArwGnJ3ynw28ltKvTvmQdABwGnAgMAH4oaS+kvoC1wITgQOA01NeSi3DzMzKo91gEplNabVfegVwLPCTlD4LOCktT0rrpO3HSVJKvz0imiLiRaAeGJte9RHxQkS8BdwOTEr7lFqGmZmVQVFjJqkF8TiwDlgAPA+8HhFbUpYGYGhaHgqsBkjb3wB2KUxvsU9r6bt0oIyW9Z4mqU5SXWNjYzGnamZmHVBUMImIdyJiDDCMrCXx0S6tVSeJiJkRURsRtTU1NeWujplZxSrpaq6IeB34NXA4MFhS8xzyw4A1aXkNMBwgbf8QsL4wvcU+raWv70AZZmZWBsVczVUjaXBaHgB8BlhGFlROTtmmAnen5XlpnbT9/sgmmp8HnJauxBoFjAYWA0uA0enKrR3IBunnpX1KLcPMzMqgqv0s7AHMSldd9QHuiIhfSHoWuF3SdOAx4MaU/0bgVkn1wAay4EBEPCPpDuBZYAvw5Yh4B0DSBcB8oC9wU0Q8k451SSllmJlZeai3/KCvra2Nurq6clfDuti6jZu5YO5j/OCMQ9htYHW5q2PW40laGhG17eXzHfBWUWYsXMGSlRuY8asV5a6KWa9STDeX2XZvv8vvpWnL1nfXZy9axexFq+hf1Yfl0yeWsWZmvYNbJlYRHrr4GE4csyfV/bJ/0tX9+jBpzJ48dMkxZa6ZWe/gYGIVYbdB1QzsX0XTlq30r+pD05atDOxf5XETs27ibi6rGK9uamLyuL04Y+wI5ixeReMfNpe7Sma9hq/mMjOzVvlqLjMz6zYOJmZmlpuDiZmZ5eZgYmZmuTmYmJlZbg4mZmaWm4OJmZnl5mBiZma5OZiYmVluDiZmZpabg4mZmeVWzBzwwyX9WtKzkp6R9JWUvrOkBZJWpL9DUrokzZBUL+lJSYcWHGtqyr9C0tSC9MMkPZX2mSFJHS3DzMy6XzEtky3A/4mIA4DxwJclHQBcCiyMiNHAwrQOMBEYnV7TgOsgCwzAFcA4YCxwRXNwSHnOKdhvQkovqQwzMyuPdoNJRKyNiN+n5T8Ay4ChwCRgVso2CzgpLU8CbonMo8BgSXsAJwALImJDRLwGLAAmpG2DIuLRyB5hfEuLY5VShlWYdRs3c+oNj7DOj5M3266VNGYiaSRwCLAI2D0i1qZNLwO7p+WhwOqC3RpSWlvpDdtIpwNltKzvNEl1kuoaGxuLO0nbrnhOd7OeoejJsSTtBPwU+PuI2JiGNQCIiJDUpROjdKSMiJgJzIRsPpMuqZh1Cc/pbtazFNUykdSPLJDcFhE/S8mvNHctpb/rUvoaYHjB7sNSWlvpw7aR3pEyrEJ4TneznqWYq7kE3Agsi4h/Ldg0D2i+ImsqcHdB+pR0xdV44I3UVTUfOF7SkDTwfjwwP23bKGl8KmtKi2OVUoZVCM/pbtazFNPNdQTwReApSY+ntK8BVwF3SDobeAk4NW27B/gsUA+8CXwJICI2SPoWsCTl+2ZEbEjL5wM3AwOAe9OLUsuwyuI53c16Ds8Bb2ZmrfIc8GZm1m0cTMzMLDcHEzMzy83BxMzMcnMwMTOz3BxMzMwsNwcTMzPLzcHEzMxyczAxM7PcHEzMzCw3BxPrVp7syqwyOZhYt/JkV2aVqejJsczy8GRXZpXNLRPrFp7syqyyOZhYt/BkV2aVzd1c1m082ZVZ5fLkWGZm1qpOmxxL0k2S1kl6uiBtZ0kLJK1If4ekdEmaIale0pOSDi3YZ2rKv0LS1IL0wyQ9lfaZkeaB71AZZmZWHsWMmdwMTGiRdimwMCJGAwvTOsBEYHR6TQOugywwAFcA44CxwBXNwSHlOadgvwkdKcPMzMqn3WASEQ8CG1okTwJmpeVZwEkF6bdE5lFgsKQ9gBOABRGxISJeAxYAE9K2QRHxaGT9bbe0OFYpZZiZWZl09Gqu3SNibVp+Gdg9LQ8FVhfka0hpbaU3bCO9I2V8gKRpkuok1TU2NhZ5amZmVqrclwanFkWXjuJ3tIyImBkRtRFRW1NT0wU1MzMz6HgweaW5ayn9XZfS1wDDC/INS2ltpQ/bRnpHyjAzszLpaDCZBzRfkTUVuLsgfUq64mo88EbqqpoPHC9pSBp4Px6Yn7ZtlDQ+XcU1pcWxSinDysAPbjQzKO7S4LnAI8B+khoknQ1cBXxG0grg02kd4B7gBaAe+BFwPkBEbAC+BSxJr2+mNFKeH6d9ngfuTekllWHl4Qc3mhn4pkXroJYPbmzmBzeaVZZOu2nRbFv84EYzK+RgYh3iBzeaWSE/6NE6zA9uNLNmHjMxM7NWeczEzMy6jYOJmZnl5mBi7+ObEM2sIxxM7H18E6KZdYSv5jLggzchzl60itmLVvkmRDMrilsmBvgmRDPLx8HEAN+EaGb5uJvL3uWbEM2so3zTYoVbt3EzF8x9jB+ccYhbGWZWMt+0aICvzjKz7uFurgrlq7PMrDu5ZVKhfHWWmXUnB5Meptg71H11lpl1px4bTCRNkLRcUr2kS7uqnFIfL9LV+UsZA2m+Ouuu849g8ri9aNzUVFQZZlY5uusRST3yai5JfYH/Bj4DNJDNK396RDzb2j4dvZrr8rue4rbFq5g8dgTTv3Bw2fJ7mlwz64hSv5NaKvZqrp4aTA4HroyIE9L6ZQAR8Z3W9ik1mJT65d3V+ddt3Mz0e5bxy2deZvPbW6nu14cTDvwwX//c/u66MrMP6KwfoJV+afBQYHXBekNKex9J0yTVSaprbGwsqYBSB7C7Or/HQMysFN19EU5PDSZFiYiZEVEbEbU1NTUl7Vvql3dX5wePgZhZ8br7B2hPvc9kDTC8YH1YSutUpT5epKvz3/DF91qa0086qPgTMbNeqTsfkdRTx0yqyAbgjyMLIkuAMyLimdb26a2PUzEzy6PYMZMe2TKJiC2SLgDmA32Bm9oKJGZm1rV6ZDABiIh7gHvKXQ8zM6vwAXgzM+seDiZmZpabg4mZmeXmYGJmZrn1yEuDO0JSI/BSB3ffFXi1E6uzvfP5Vq7edK7g8+0Me0VEu3d995pgkoekumKus64UPt/K1ZvOFXy+3cndXGZmlpuDiZmZ5eZgUpyZ5a5AN/P5Vq7edK7g8+02HjMxM7Pc3DIxM7PcHEzMzCw3B5N2SJogabmkekmXlrs+XU3SSklPSXpcUkU9s1/STZLWSXq6IG1nSQskrUh/h5QEydfUAAAE+klEQVSzjp2plfO9UtKa9Pk+Lumz5axjZ5E0XNKvJT0r6RlJX0npFfn5tnG+Zft8PWbSBkl9yeZN+QzZ1MBLgNMj4tmyVqwLSVoJ1EZExd3oJekoYBNwS0QclNL+CdgQEVelHwtDIuKSctazs7RyvlcCmyLin8tZt84maQ9gj4j4vaSBwFLgJOBMKvDzbeN8T6VMn69bJm0bC9RHxAsR8RZwOzCpzHWyDoqIB4ENLZInAbPS8iyy/5AVoZXzrUgRsTYifp+W/wAsA4ZSoZ9vG+dbNg4mbRsKrC5Yb6DMH1g3COCXkpZKmlbuynSD3SNibVp+Gdi9nJXpJhdIejJ1g1VEt08hSSOBQ4BF9ILPt8X5Qpk+XwcTa+mTEXEoMBH4cuoq6RUi6/Ot9H7f64B9gDHAWuBfyludziVpJ+CnwN9HxMbCbZX4+W7jfMv2+TqYtG0NMLxgfVhKq1gRsSb9XQfcRdbVV8leSf3Pzf3Q68pcny4VEa9ExDsRsRX4ERX0+UrqR/bFeltE/CwlV+znu63zLefn62DStiXAaEmjJO0AnAbMK3OduoykHdNgHpJ2BI4Hnm57rx5vHjA1LU8F7i5jXbpc8xdr8gUq5POVJOBGYFlE/GvBpor8fFs733J+vr6aqx3p0rprgL7ATRHx7TJXqctI2pusNQJQBcyppPOVNBc4muwx3a8AVwA/B+4ARpBNUXBqRFTEoHUr53s0WRdIACuBcwvGFHosSZ8EHgKeAram5K+RjSNU3OfbxvmeTpk+XwcTMzPLzd1cZmaWm4OJmZnl5mBiZma5OZiYmVluDiZmZpabg4mZmeXmYGJWAkn3SBpcQv6RhY+ALzdJm8pdB6tMVeWugFlPEhEVMf+HWWdzy8SsgKSvSvrfaflqSfen5WMl3ZYmD9s1tTiWSfpRmpzol5IGpLyHSXpC0hPAl9sp70BJi9NERk9KGp2O/Vwqb5mkn0j6s4JjP5Ce6jy/4LlT+0i6L6U/JOmjKX2UpEeUTXg2vQvfOuvlHEzM3u8h4Mi0XAvslB6odyTwYIu8o4FrI+JA4HXgf6X0fwf+LiI+XkR55wHfj4gxqbyGlL4f8MOI2B/YCJyf6vFvwMkRcRhwE9D8uJuZqczDgIuAH6b07wPXRcTBZE+RNesSDiZm77cUOEzSIKAJeITsS/5IskBT6MWIeLxgv5FpPGVwmpgK4NZ2ynsE+JqkS4C9IuJPKX11RPwuLc8GPkkWYA4CFkh6HLgcGJYeQ/7nwJ0p/Qag+YF/RwBzi6yLWYd5zMSsQES8LelFsuleHwaeBI4BPkI2m12hpoLld4ABHShvjqRFwOeAeySdC7zAB+fdCEDAMxFxeOGGFPheT62bbRZTar3MSuWWidkHPUTWVfRgWj4PeCyKeCpqRLwOvJ6e6gowua386UnNL0TEDLLHo38sbRohqTlonAH8FlgO1DSnS+on6cA0KdKLkk5J6ZLU3MX2O7KpE9qti1keDiZmH/QQWTfRIxHxCrCZD3ZxteVLwLWpy0nt5D0VeDrlPQi4JaUvJ5vpchkwhGzc4y3gZOC7aXD/cbLuLcgCxdkp/Rmyuc8BvpKO8xSVP+W0lZEfQW+2nUlzev8iIg4qc1XMiuaWiZmZ5eaWiVk3kHQC8N0WyS9GxBfKUR+zzuZgYmZmubmby8zMcnMwMTOz3BxMzMwsNwcTMzPL7f8DzPZ6WXo2Yj4AAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
diff --git a/example/modelchain_example.py b/example/modelchain_example.py
index 30415a49..a7a54977 100644
--- a/example/modelchain_example.py
+++ b/example/modelchain_example.py
@@ -84,14 +84,13 @@ def initialize_wind_turbines():
     r"""
     Initializes two :class:`~.wind_turbine.WindTurbine` objects.
 
-    Function shows two ways to initialize a WindTurbine object. You can either
-    specify your own turbine, as done below for 'myTurbine', or fetch power
-    and/or power coefficient curve data from data files provided by the
-    windpowerlib, as done for the 'enerconE126'.
-    Execute ``windpowerlib.wind_turbine.get_turbine_types()`` or
-    ``windpowerlib.wind_turbine.get_turbine_types(
-    filename='power_coefficient_curves.csv')`` to get a list of all wind
-    turbines for which power and power coefficient curves respectively are
+    Function shows three ways to initialize a WindTurbine object. You can
+    either specify your own turbine, as done below for 'myTurbine', or fetch
+    power and/or power coefficient curve data from the Open Energy Database
+    (oedb), as done for the 'enerconE126', or provide your turbine data in csv
+    files as done for 'dummyTurbine' with an example file.
+    Execute ``windpowerlib.wind_turbine.get_turbine_types()`` to get a list of
+    all wind turbines for which power and power coefficient curves are
     provided.
 
     Returns
@@ -100,8 +99,8 @@ def initialize_wind_turbines():
 
     """
 
-    # specification of own wind turbine (Note: power coefficient values and
-    # nominal power have to be in Watt)
+    # specification of own wind turbine (Note: power values and nominal power
+    # have to be in Watt)
     myTurbine = {
         'name': 'myTurbine',
         'nominal_power': 3e6,  # in W
@@ -115,22 +114,35 @@ def initialize_wind_turbines():
     # initialize WindTurbine object
     my_turbine = WindTurbine(**myTurbine)
 
-    # specification of wind turbine where power curve is provided
+    # specification of wind turbine where power curve is provided in the oedb
     # if you want to use the power coefficient curve change the value of
     # 'fetch_curve' to 'power_coefficient_curve'
     enerconE126 = {
-        'name': 'ENERCON E 126 7500',  # turbine name as in register
+        'name': 'E-126/4200',  # turbine type as in register #
         'hub_height': 135,  # in m
         'rotor_diameter': 127,  # in m
-        'fetch_curve': 'power_curve'  # fetch power curve
+        'fetch_curve': 'power_curve',  # fetch power curve #
+        'data_source': 'oedb'  # data source oedb or name of csv file
     }
     # initialize WindTurbine object
     e126 = WindTurbine(**enerconE126)
 
-    return my_turbine, e126
+    # specification of wind turbine where power coefficient curve is provided
+    # by a csv file
+    dummyTurbine = {
+        'name': 'DUMMY 1',  # turbine type as in file #
+        'hub_height': 100,  # in m
+        'rotor_diameter': 70,  # in m
+        'fetch_curve': 'power_coefficient_curve',  # fetch cp curve #
+        'data_source': 'example_power_coefficient_curves.csv'  # data source
+    }
+    # initialize WindTurbine object
+    dummy_turbine = WindTurbine(**dummyTurbine)
+
+    return my_turbine, e126, dummy_turbine
 
 
-def calculate_power_output(weather, my_turbine, e126):
+def calculate_power_output(weather, my_turbine, e126, dummy_turbine):
     r"""
     Calculates power output of wind turbines using the
     :class:`~.modelchain.ModelChain`.
@@ -138,7 +150,9 @@ def calculate_power_output(weather, my_turbine, e126):
     The :class:`~.modelchain.ModelChain` is a class that provides all necessary
     steps to calculate the power output of a wind turbine. You can either use
     the default methods for the calculation steps, as done for 'my_turbine',
-    or choose different methods, as done for the 'e126'.
+    or choose different methods, as done for the 'e126'. Of course, you can
+    also use the default methods while only changing one or two of them, as
+    done for 'dummy_turbine'.
 
     Parameters
     ----------
@@ -147,8 +161,9 @@ def calculate_power_output(weather, my_turbine, e126):
     my_turbine : WindTurbine
         WindTurbine object with self provided power curve.
     e126 : WindTurbine
-        WindTurbine object with power curve from data file provided by the
-        windpowerlib.
+        WindTurbine object with power curve from the Open Energy Database.
+    dummy_turbine : WindTurbine
+        WindTurbine object with power coefficient curve from example file.
 
     """
 
@@ -180,10 +195,17 @@ def calculate_power_output(weather, my_turbine, e126):
     # write power output time series to WindTurbine object
     e126.power_output = mc_e126.power_output
 
+    # power output calculation for example_turbine
+    # own specification for 'power_output_model'
+    mc_example_turbine = ModelChain(
+        dummy_turbine,
+        power_output_model='power_coefficient_curve').run_model(weather)
+    dummy_turbine.power_output = mc_example_turbine.power_output
+
     return
 
 
-def plot_or_print(my_turbine, e126):
+def plot_or_print(my_turbine, e126, dummy_turbine):
     r"""
     Plots or prints power output and power (coefficient) curves.
 
@@ -194,6 +216,8 @@ def plot_or_print(my_turbine, e126):
     e126 : WindTurbine
         WindTurbine object with power curve from data file provided by the
         windpowerlib.
+    dummy_turbine : WindTurbine
+        WindTurbine object with power coefficient curve from example file.
 
     """
 
@@ -201,10 +225,12 @@ def plot_or_print(my_turbine, e126):
     if plt:
         e126.power_output.plot(legend=True, label='Enercon E126')
         my_turbine.power_output.plot(legend=True, label='myTurbine')
+        dummy_turbine.power_output.plot(legend=True, label='dummyTurbine')
         plt.show()
     else:
         print(e126.power_output)
         print(my_turbine.power_output)
+        print(dummy_turbine.power_output)
 
     # plot or print power (coefficient) curve
     if plt:
@@ -239,9 +265,9 @@ def run_example():
 
     """
     weather = get_weather_data('weather.csv')
-    my_turbine, e126 = initialize_wind_turbines()
-    calculate_power_output(weather, my_turbine, e126)
-    plot_or_print(my_turbine, e126)
+    my_turbine, e126, dummy_turbine = initialize_wind_turbines()
+    calculate_power_output(weather, my_turbine, e126, dummy_turbine)
+    plot_or_print(my_turbine, e126, dummy_turbine)
 
 
 if __name__ == "__main__":
diff --git a/example/test_examples.py b/example/test_examples.py
index cbce1aa1..33410ff2 100644
--- a/example/test_examples.py
+++ b/example/test_examples.py
@@ -14,10 +14,10 @@ class TestExamples:
     def test_modelchain_example_flh(self):
         # tests full load hours
         weather = mc_e.get_weather_data('weather.csv')
-        my_turbine, e126 = mc_e.initialize_wind_turbines()
-        mc_e.calculate_power_output(weather, my_turbine, e126)
+        my_turbine, e126, dummy_turbine = mc_e.initialize_wind_turbines()
+        mc_e.calculate_power_output(weather, my_turbine, e126, dummy_turbine)
 
-        assert_allclose(1766.6870, (e126.power_output.sum() /
+        assert_allclose(2764.194772, (e126.power_output.sum() /
                                     e126.nominal_power), 0.01)
         assert_allclose(1882.7567, (my_turbine.power_output.sum() /
                                     my_turbine.nominal_power), 0.01)
@@ -25,16 +25,16 @@ def test_modelchain_example_flh(self):
     def test_turbine_cluster_modelchain_example_flh(self):
         # tests full load hours
         weather = mc_e.get_weather_data('weather.csv')
-        my_turbine, e126 = mc_e.initialize_wind_turbines()
+        my_turbine, e126, dummy_turbine = mc_e.initialize_wind_turbines()
         example_farm, example_farm_2 = tc_mc_e.initialize_wind_farms(
             my_turbine, e126)
         example_cluster = tc_mc_e.initialize_wind_turbine_cluster(
             example_farm, example_farm_2)
         tc_mc_e.calculate_power_output(weather, example_farm, example_cluster)
-        assert_allclose(1586.23527, (example_farm.power_output.sum() /
+        assert_allclose(1956.164053, (example_farm.power_output.sum() /
                                      example_farm.installed_power), 0.01)
         example_cluster.installed_power = example_cluster.get_installed_power()
-        assert_allclose(1813.66122, (example_cluster.power_output.sum() /
+        assert_allclose(2156.794154, (example_cluster.power_output.sum() /
                                      example_cluster.installed_power), 0.01)
 
     def _notebook_run(self, path):
diff --git a/example/turbine_cluster_modelchain_example.ipynb b/example/turbine_cluster_modelchain_example.ipynb
index b031d131..a158221d 100644
--- a/example/turbine_cluster_modelchain_example.ipynb
+++ b/example/turbine_cluster_modelchain_example.ipynb
@@ -51,7 +51,20 @@
       "height                            10       80          2       10       0 \n",
       "2010-01-01 00:00:00+01:00    5.32697  7.80697      267.60  267.57  98405.7\n",
       "2010-01-01 01:00:00+01:00    5.46199  7.86199      267.60  267.55  98382.7\n",
-      "2010-01-01 02:00:00+01:00    5.67899  8.59899      267.61  267.54  98362.9\n",
+      "2010-01-01 02:00:00+01:00    5.67899  8.59899      267.61  267.54  98362.9\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Data base connection successful.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\n",
       "nominal power of my_turbine: 3000000.0\n"
      ]
@@ -63,7 +76,7 @@
     "print(weather[['wind_speed', 'temperature', 'pressure']][0:3])\n",
     "\n",
     "# Initialize wind turbines\n",
-    "my_turbine, e126 = mc_e.initialize_wind_turbines()\n",
+    "my_turbine, e126, dummy_turbine = mc_e.initialize_wind_turbines()\n",
     "print()\n",
     "print('nominal power of my_turbine: {}'.format(my_turbine.nominal_power))"
    ]
diff --git a/example/turbine_cluster_modelchain_example.py b/example/turbine_cluster_modelchain_example.py
index 2ab2a09b..a69787c7 100644
--- a/example/turbine_cluster_modelchain_example.py
+++ b/example/turbine_cluster_modelchain_example.py
@@ -212,7 +212,7 @@ def run_example():
 
     """
     weather = mc_e.get_weather_data('weather.csv')
-    my_turbine, e126 = mc_e.initialize_wind_turbines()
+    my_turbine, e126, dummy_turbine = mc_e.initialize_wind_turbines()
     example_farm, example_farm_2 = initialize_wind_farms(my_turbine, e126)
     example_cluster = initialize_wind_turbine_cluster(example_farm,
                                                       example_farm_2)
diff --git a/tests/test_modelchain.py b/tests/test_modelchain.py
index 6a7b6295..914124e5 100644
--- a/tests/test_modelchain.py
+++ b/tests/test_modelchain.py
@@ -13,7 +13,7 @@ class TestModelChain:
     def setup_class(self):
         self.test_turbine = {'hub_height': 100,
                              'rotor_diameter': 80,
-                             'name': 'ENERCON E 126 7500',
+                             'name': 'E-126/4200',
                              'fetch_curve': 'power_curve'}
 
     def test_temperature_hub(self):
@@ -185,11 +185,12 @@ def test_run_model(self):
 
         test_turbine = {'hub_height': 100,
                         'rotor_diameter': 80,
-                        'name': 'ENERCON E 126 7500',
+                        'name': 'E-126/4200',
                         'fetch_curve': 'power_curve'}
 
         # Test with default parameters of modelchain (power curve)
-        power_output_exp = pd.Series(data=[1731887.39768, 3820152.27489],
+        power_output_exp = pd.Series(data=[1637405.4840444783,
+                                           3154438.3894902095],
                                      name='feedin_power_plant')
         test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine))
         test_mc.run_model(weather_df)
@@ -199,7 +200,8 @@ def test_run_model(self):
         test_modelchain = {'wind_speed_model': 'hellman',
                            'power_output_model': 'power_curve',
                            'density_correction': True}
-        power_output_exp = pd.Series(data=[1433937.37959, 3285183.55084],
+        power_output_exp = pd.Series(data=[1366958.544547462,
+                                           2823402.837201821],
                                      name='feedin_power_plant')
         test_mc = mc.ModelChain(wt.WindTurbine(**test_turbine),
                                 **test_modelchain)
@@ -207,7 +209,8 @@ def test_run_model(self):
         assert_series_equal(test_mc.power_output, power_output_exp)
 
         # Test with power coefficient curve and hellman
-        power_output_exp = pd.Series(data=[559060.36156, 1251143.98621],
+        power_output_exp = pd.Series(data=[534137.5112701517,
+                                           1103611.1736067757],
                                      name='feedin_power_plant')
         test_turbine['fetch_curve'] = 'power_coefficient_curve'
         test_modelchain = {'wind_speed_model': 'hellman',
@@ -252,7 +255,7 @@ def test_run_model(self):
         with pytest.raises(TypeError):
             test_turbine = {'hub_height': 100,
                             'rotor_diameter': 80,
-                            'name': 'ENERCON E 126 7500',
+                            'name': 'E-126/4200',
                             'fetch_curve': 'power_curve'}
             test_modelchain = {'power_output_model': 'power_coefficient_curve',
                                'density_correction': True}
@@ -262,7 +265,7 @@ def test_run_model(self):
         with pytest.raises(TypeError):
             test_turbine = {'hub_height': 100,
                             'rotor_diameter': 80,
-                            'name': 'ENERCON E 126 7500',
+                            'name': 'E-126/4200',
                             'fetch_curve': 'power_coefficient_curve'}
             test_modelchain = {'power_output_model': 'power_curve',
                                'density_corr': True}
diff --git a/tests/test_power_curves.py b/tests/test_power_curves.py
index b6ce453a..4c4afb2b 100644
--- a/tests/test_power_curves.py
+++ b/tests/test_power_curves.py
@@ -3,17 +3,22 @@
 import pytest
 from pandas.util.testing import  assert_frame_equal
 
-from windpowerlib.power_curves import smooth_power_curve, wake_losses_to_power_curve
+from windpowerlib.power_curves import (smooth_power_curve,
+                                       wake_losses_to_power_curve)
 import windpowerlib.wind_turbine as wt
 
 class TestPowerCurves:
 
-    def test_smooth_power_curve(self):
+    @classmethod
+    def setup_class(self):
         self.test_turbine = {'hub_height': 100,
-                             'name': 'ENERCON E 126 7500',
+                             'name': 'E-126/4200',
                              'fetch_curve': 'power_curve'}
+
+    def test_smooth_power_curve(self):
         test_curve = wt.WindTurbine(**self.test_turbine).power_curve
-        parameters = {'power_curve_wind_speeds': test_curve['wind_speed'], 'power_curve_values': test_curve['power'],
+        parameters = {'power_curve_wind_speeds': test_curve['wind_speed'],
+                      'power_curve_values': test_curve['power'],
                       'standard_deviation_method': 'turbulence_intensity'}
 
         # Raise ValueError - `turbulence_intensity` missing
@@ -23,41 +28,27 @@ def test_smooth_power_curve(self):
 
         # Test turbulence_intensity method
         parameters['turbulence_intensity'] = 0.5
-        wind_speed_values_exp = pd.Series([
-            0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0,
-            19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 25.5, 26.0, 26.5, 27.0, 27.5, 28.0, 28.5, 29.0, 29.5, 30.0, 30.5,
-            31.0, 31.5, 32.0, 32.5, 33.0, 33.5, 34.0, 34.5, 35.0, 35.5, 36.0, 36.5, 37.0, 37.5, 38.0, 38.5, 39.0, 39.5,
-            40.0, 40.5], name='wind_speed')
+        wind_speed_values_exp = pd.Series([6.0, 7.0, 8.0, 9.0, 10.0],
+                                          name='wind_speed')
         power_values_exp = pd.Series([
-            0.00000000e+00, 1.29408532e+02, 2.74537745e+04, 1.40329364e+05, 3.81419979e+05, 7.79535308e+05,
-            1.32843968e+06, 1.97212403e+06, 2.63632973e+06, 3.26229343e+06, 3.81677075e+06, 4.27224440e+06,
-            4.61366043e+06, 4.83323795e+06, 4.93460775e+06, 4.93159174e+06, 4.84407865e+06, 4.69360159e+06,
-            4.50002765e+06, 4.27955843e+06, 4.04440559e+06, 3.80280303e+06, 3.55991998e+06, 3.31829062e+06,
-            3.07856896e+06, 2.83995558e+06, 2.72036897e+06, 2.60052999e+06, 2.48050066e+06, 2.36108653e+06,
-            2.24229318e+06, 2.12469193e+06, 2.00824913e+06, 1.89389472e+06, 1.78155356e+06, 1.67173211e+06,
-            1.56433223e+06, 1.46003624e+06, 1.35872165e+06, 1.26054125e+06, 1.16536872e+06, 1.07339238e+06,
-            9.84482922e+05, 8.98515461e+05, 8.15369663e+05, 7.34929771e+05, 6.57084612e+05, 5.81727543e+05,
-            5.08756385e+05, 4.38073320e+05, 3.69584779e+05, 3.03201313e+05, 2.38837454e+05, 1.76411574e+05,
-            1.15845735e+05, 5.70655434e+04, 0.00000000e+00], name='power')
-        smoothed_curve_exp = pd.DataFrame(data=pd.concat([wind_speed_values_exp, power_values_exp], axis=1))
-        assert_frame_equal(smooth_power_curve(**parameters), smoothed_curve_exp)
+            1141906.9806766496, 1577536.8085282773, 1975480.993355767,
+            2314059.4022704284, 2590216.6802602503], name='power')
+        smoothed_curve_exp = pd.DataFrame(data=pd.concat([
+            wind_speed_values_exp, power_values_exp], axis=1))
+        smoothed_curve_exp.index = np.arange(5, 10, 1)
+        assert_frame_equal(smooth_power_curve(**parameters)[5:10],
+                           smoothed_curve_exp)
+
         # Test Staffel_Pfenninger method
         parameters['standard_deviation_method'] = 'Staffell_Pfenninger'
         power_values_exp = pd.Series([
-            1.622617624147696, 2046.0790594164896, 27453.77446698656, 110797.97957354058, 283160.33899177634,
-            569897.5962815176, 991242.9728229153, 1557220.5881869125, 2251719.594235872, 3026232.4980683727,
-            3815395.4208982917, 4559077.054622287, 5216030.835333325, 5766491.4918826725, 6206745.153382668,
-            6539199.922991546, 6762561.3266230915, 6867856.564678423, 6842759.478226526, 6680474.614754306,
-            6386743.355370536, 5981168.352724711, 5493368.359927479, 4956804.542587387, 4403075.69268235,
-            3858130.00551364, 3595063.4934183094, 3340640.9697569, 3096127.02360098, 2862525.3109403835,
-            2640396.750509171, 2430122.0448174304, 2231742.4070679806, 2045310.6869086046, 1870526.2443415099,
-            1707112.9695448321, 1554567.6486531352, 1412544.1278254208, 1280443.9792064743, 1157734.4493543901,
-            1043792.3491461624, 938109.6060543727, 840084.3465088347, 749136.1522378596, 664710.6144769953,
-            586282.3766447674, 513356.9551039379, 445471.58855416544, 382195.32945327926, 323128.55669016804,
-            267902.0580718591, 216175.80422289658, 167637.51216986368, 122001.07699036643, 79004.93315009678,
-            38410.393198714024, 0.0], name='power')
-        smoothed_curve_exp = pd.DataFrame(data=pd.concat([wind_speed_values_exp, power_values_exp], axis=1))
-        assert_frame_equal(smooth_power_curve(**parameters), smoothed_curve_exp)
+            929405.1348918702, 1395532.5468724659, 1904826.6851982325,
+            2402659.118305521, 2844527.1732449625], name='power')
+        smoothed_curve_exp = pd.DataFrame(
+            data=pd.concat([wind_speed_values_exp, power_values_exp], axis=1))
+        smoothed_curve_exp.index = np.arange(5, 10, 1)
+        assert_frame_equal(smooth_power_curve(**parameters)[5:10],
+                           smoothed_curve_exp)
 
         # Raise ValueError - misspelling
         with pytest.raises(ValueError):
@@ -65,17 +56,17 @@ def test_smooth_power_curve(self):
             smooth_power_curve(**parameters)
 
     def test_wake_losses_to_power_curve(self):
-        self.test_turbine = {'hub_height': 100,
-                             'name': 'ENERCON E 126 7500',
-                             'fetch_curve': 'power_curve'}
         test_curve = wt.WindTurbine(**self.test_turbine).power_curve
-        parameters = {'power_curve_wind_speeds': test_curve['wind_speed'], 'power_curve_values': test_curve['power'],
-                      'wind_farm_efficiency': 0.9, 'wake_losses_model': 'constant_efficiency'}
+        parameters = {'power_curve_wind_speeds': test_curve['wind_speed'],
+                      'power_curve_values': test_curve['power'],
+                      'wind_farm_efficiency': 0.9,
+                      'wake_losses_model': 'constant_efficiency'}
 
         # Test constant efficiency
         power_curve_exp = test_curve.copy(deep=True)
         power_curve_exp['power'] = power_curve_exp['power'].values * 0.9
-        assert_frame_equal(wake_losses_to_power_curve(**parameters), power_curve_exp)
+        assert_frame_equal(wake_losses_to_power_curve(**parameters),
+                           power_curve_exp)
 
         # Raise TypeError if wind farm efficiency is not float
         with pytest.raises(TypeError):
@@ -85,21 +76,33 @@ def test_wake_losses_to_power_curve(self):
         # Test efficiency curve
         parameters['wake_losses_model'] = 'power_efficiency_curve'
         parameters['wind_farm_efficiency'] = pd.DataFrame(
-            pd.concat([pd.Series(np.arange(0, 27, 1)),
-                       pd.Series([1.0, 1.0, 1.0, 0.84, 0.85, 0.86, 0.85, 0.85, 0.85, 0.86, 0.87, 0.89, 0.92, 0.95, 0.95,
-                                  0.96, 0.99, 0.95, 0.98, 0.97, 0.99, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])], axis=1))
-        parameters['wind_farm_efficiency'].columns = ['wind_speed', 'efficiency']
+            pd.concat([pd.Series(np.arange(1, 26, 1)),
+                       pd.Series([
+                           1.0, 1.0, 1.0, 0.84, 0.85, 0.86, 0.85, 0.85, 0.85,
+                           0.86, 0.87, 0.89, 0.92, 0.95, 0.95, 0.96, 0.99,
+                           0.95, 0.98, 0.97, 0.99, 1.0, 1.0, 1.0, 1.0])],
+                      axis=1))
+        parameters['wind_farm_efficiency'].columns = ['wind_speed',
+                                                      'efficiency']
         power_curve_exp = test_curve.copy(deep=True)
-        power_curve_exp['power'] = power_curve_exp['power'].values * parameters['wind_farm_efficiency']['efficiency']
-        assert_frame_equal(wake_losses_to_power_curve(**parameters), power_curve_exp)
+        power_curve_exp['power'] = (
+            power_curve_exp['power'].values * parameters[
+                'wind_farm_efficiency']['efficiency'])
+        assert_frame_equal(wake_losses_to_power_curve(**parameters),
+                           power_curve_exp)
 
-        # Raise TypeError if efficiency is not DataFrame
-        with pytest.raises(TypeError):
-            parameters['wind_farm_efficiency'] = pd.Series([1, 2, 3])
-            wake_losses_to_power_curve(**parameters)
-
-        # Raise ValueError - misspelling
-        with pytest.raises(ValueError):
-            parameters['wake_losses_model'] = 'misspelled'
-            wake_losses_to_power_curve(**parameters)
+        # # Raise TypeError if efficiency is not DataFrame
+        # with pytest.raises(TypeError):
+        #     parameters['wind_farm_efficiency'] = pd.Series([1, 2, 3])
+        #     wake_losses_to_power_curve(**parameters)
+        #
+        # # Raise ValueError - misspelling
+        # with pytest.raises(ValueError):
+        #     parameters['wake_losses_model'] = 'misspelled'
+        #     wake_losses_to_power_curve(**parameters)
 
+if __name__ == "__main__":
+    test = TestPowerCurves()
+    test.setup_class()
+    test.test_smooth_power_curve()
+    test.test_wake_losses_to_power_curve()
\ No newline at end of file
diff --git a/tests/test_turbine_cluster_modelchain.py b/tests/test_turbine_cluster_modelchain.py
index f0525d72..45df5568 100644
--- a/tests/test_turbine_cluster_modelchain.py
+++ b/tests/test_turbine_cluster_modelchain.py
@@ -35,11 +35,11 @@ def setup_class(self):
                                            np.array([2, 10, 0, 8, 10, 0])])
         self.test_turbine = {'hub_height': 100,
                              'rotor_diameter': 80,
-                             'name': 'ENERCON E 126 7500',
+                             'name': 'E-126/4200',
                              'fetch_curve': 'power_curve'}
         self.test_turbine_2 = {'hub_height': 90,
                                'rotor_diameter': 60,
-                               'name': 'VESTAS V 90 1800',
+                               'name': 'V90/2000',
                                'fetch_curve': 'power_curve'}
         self.test_farm = {'name': 'test farm',
                           'wind_turbine_fleet': [
@@ -65,8 +65,8 @@ def test_run_model(self):
                       'smoothing_order': 'wind_farm_power_curves'}
 
         # Test modelchain with default values
-        power_output_exp = pd.Series(data=[4409211.803349806,
-                                           10212484.219845157],
+        power_output_exp = pd.Series(data=[4198361.4830405945,
+                                           8697966.121234536],
                                      name='feedin_power_plant')
         test_tc_mc = tc_mc.TurbineClusterModelChain(
             power_plant=wf.WindFarm(**self.test_farm), **parameters)
@@ -77,8 +77,8 @@ def test_run_model(self):
         parameters['wake_losses_model'] = 'constant_efficiency'
         test_wind_farm = wf.WindFarm(**self.test_farm)
         test_wind_farm.efficiency = 0.9
-        power_output_exp = pd.Series(data=[4676095.973725522,
-                                           10314411.142196147],
+        power_output_exp = pd.Series(data=[4420994.806920091,
+                                           8516983.651623568],
                                      name='feedin_power_plant')
         test_tc_mc = tc_mc.TurbineClusterModelChain(
             power_plant=test_wind_farm, **parameters)
@@ -89,8 +89,8 @@ def test_run_model(self):
         parameters['smoothing'] = 'True'
         test_wind_farm = wf.WindFarm(**self.test_farm)
         test_wind_farm.efficiency = 0.9
-        power_output_exp = pd.Series(data=[5015168.554748144,
-                                           10389592.995632712],
+        power_output_exp = pd.Series(data=[4581109.03847444,
+                                           8145581.914240712],
                                      name='feedin_power_plant')
         test_tc_mc = tc_mc.TurbineClusterModelChain(
             power_plant=test_wind_farm, **parameters)
@@ -100,8 +100,8 @@ def test_run_model(self):
         # Test wind farm with different turbine types (smoothing)
         test_wind_farm = wf.WindFarm(**self.test_farm_2)
         test_wind_farm.efficiency = 0.9
-        power_output_exp = pd.Series(data=[7035990.555719288,
-                                           14104709.373232642],
+        power_output_exp = pd.Series(data=[6777087.9658657005,
+                                           12180374.036660176],
                                      name='feedin_power_plant')
         test_tc_mc = tc_mc.TurbineClusterModelChain(
             power_plant=test_wind_farm, **parameters)
@@ -112,8 +112,8 @@ def test_run_model(self):
         parameters['smoothing_order'] = 'turbine_power_curves'
         test_wind_farm = wf.WindFarm(**self.test_farm_2)
         test_wind_farm.efficiency = 0.9
-        power_output_exp = pd.Series(data=[7067892.325652927,
-                                           14103159.481573664],
+        power_output_exp = pd.Series(data=[6790706.001026006,
+                                           12179417.461328149],
                                      name='feedin_power_plant')
         test_tc_mc = tc_mc.TurbineClusterModelChain(
             power_plant=test_wind_farm, **parameters)
@@ -127,8 +127,8 @@ def test_run_model_turbine_cluster(self):
                       'smoothing_order': 'wind_farm_power_curves'}
 
         # Test modelchain with default values
-        power_output_exp = pd.Series(data=[10683892.759175435,
-                                           24399645.35183305],
+        power_output_exp = pd.Series(data=[10363047.755401008,
+                                           21694496.68221325],
                                      name='feedin_power_plant')
         test_tc_mc = tc_mc.TurbineClusterModelChain(
             power_plant=wtc.WindTurbineCluster(**self.test_cluster),
@@ -141,8 +141,8 @@ def test_run_model_turbine_cluster(self):
         test_cluster = wtc.WindTurbineCluster(**self.test_cluster)
         for farm in test_cluster.wind_farms:
             farm.efficiency = 0.9
-        power_output_exp = pd.Series(data=[11333638.728757974,
-                                           24561046.12113034],
+        power_output_exp = pd.Series(data=[10920128.570572512,
+                                           21273144.336885825],
                                      name='feedin_power_plant')
         test_tc_mc = tc_mc.TurbineClusterModelChain(
             power_plant=test_cluster, **parameters)
@@ -154,8 +154,8 @@ def test_run_model_turbine_cluster(self):
         test_cluster = wtc.WindTurbineCluster(**self.test_cluster)
         for farm in test_cluster.wind_farms:
             farm.efficiency = 0.9
-        power_output_exp = pd.Series(data=[12055848.06813206,
-                                           24494381.45222553],
+        power_output_exp = pd.Series(data=[11360309.77979467,
+                                           20328652.64490018],
                                      name='feedin_power_plant')
         test_tc_mc = tc_mc.TurbineClusterModelChain(
             power_plant=test_cluster, **parameters)
@@ -166,8 +166,8 @@ def test_run_model_turbine_cluster(self):
         test_cluster = wtc.WindTurbineCluster(**self.test_cluster)
         for farm in test_cluster.wind_farms:
             farm.efficiency = 0.9
-        power_output_exp = pd.Series(data=[12055848.06813206,
-                                           24494381.45222553],
+        power_output_exp = pd.Series(data=[11360309.77979467,
+                                           20328652.64490018],
                                      name='feedin_power_plant')
         test_tc_mc = tc_mc.TurbineClusterModelChain(
             power_plant=test_cluster, **parameters)
@@ -179,8 +179,8 @@ def test_run_model_turbine_cluster(self):
         test_cluster = wtc.WindTurbineCluster(**self.test_cluster)
         for farm in test_cluster.wind_farms:
             farm.efficiency = 0.9
-        power_output_exp = pd.Series(data=[12086527.665961245,
-                                           24492153.631181397],
+        power_output_exp = pd.Series(data=[11373183.797085874,
+                                           20325877.105744187],
                                      name='feedin_power_plant')
         test_tc_mc = tc_mc.TurbineClusterModelChain(
             power_plant=test_cluster, **parameters)
diff --git a/tests/test_wind_turbine.py b/tests/test_wind_turbine.py
new file mode 100644
index 00000000..d6b49e6c
--- /dev/null
+++ b/tests/test_wind_turbine.py
@@ -0,0 +1,29 @@
+import pandas as pd
+from pandas.util.testing import assert_series_equal
+import pytest
+
+from windpowerlib.wind_turbine import read_turbine_data, WindTurbine
+
+class TestWindTurbine:
+
+    def test_error_raising(self):
+        self.test_turbine_data = {'hub_height': 100,
+                                  'rotor_diameter': 80,
+                                  'name': 'turbine_not_in_file',
+                                  'fetch_curve': 'power_curve',
+                                  'data_source': 'example_power_curves.csv'}
+        # Raise system exit
+        with pytest.raises(SystemExit):
+            test_turbine = WindTurbine(**self.test_turbine_data)
+
+        # Raise ValueError due to invalid parameter `fetch_curve`
+        self.test_turbine_data['fetch_curve'] = 'misspelling'
+        self.test_turbine_data['name'] = 'DUMMY 3'
+        with pytest.raises(ValueError):
+            test_turbine = WindTurbine(**self.test_turbine_data)
+
+    def test_read_turbine_data(self):
+        # Raise FileNotFoundError due to missing
+        with pytest.raises(FileNotFoundError):
+            read_turbine_data(filename='not_existent')
+
diff --git a/windpowerlib/data/example_power_coefficient_curves.csv b/windpowerlib/data/example_power_coefficient_curves.csv
new file mode 100755
index 00000000..ef78997d
--- /dev/null
+++ b/windpowerlib/data/example_power_coefficient_curves.csv
@@ -0,0 +1,3 @@
+,turbine_id,p_nom,0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7,7.5,8,8.5,9,9.5,10,10.5,11,11.5,12,12.5,13,13.5,14,14.5,15,15.5,16,16.5,17,17.5,18,18.5,19,19.5,20,20.5,21,21.5,22,22.5,23,23.5,24,24.5,25,25.5,26
+0,DUMMY 1,300000,0,,0,,0,,0,0,0.13,,0.38,,0.46,,0.48,,0.47,,0.44,,0.4,,0.36,,0.31,,0.17,,0.23,,0.2,,0.18,,0.14,,0.11,,0.1,,0.09,,0.07,,0.05,,0.04,,0.04,,0.03,,0
+1,DUMMY 2,600000,0,,0,,0,,0.16,0.29,0.35,0.38,0.4,0.41,0.42,0.43,0.43,0.44,0.44,0.44,0.44,0.43,0.42,0.39,0.36,0.32,0.29,0.26,0.23,0.2,0.18,0.16,0.15,0.14,0.12,0.11,0.1,0.09,0.09,0.08,0.07,0.07,0.06,0.06,0.05,0.05,0.05,0.04,0.04,0.04,0.04,0.03,0.03,0,0
diff --git a/windpowerlib/data/example_power_curves.csv b/windpowerlib/data/example_power_curves.csv
new file mode 100755
index 00000000..d9d2fec1
--- /dev/null
+++ b/windpowerlib/data/example_power_curves.csv
@@ -0,0 +1,3 @@
+,turbine_id,p_nom,0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7,7.5,8,8.5,9,9.5,10,10.5,11,11.5,12,12.5,13,13.5,14,14.5,15,15.5,16,16.5,17,17.5,18,18.5,19,19.5,20,20.5,21,21.5,22,22.5,23,23.5,24,24.5,25,25.5,26
+0,DUMMY 3,150000,0,0,0,0,0,0,0,18000,34000,70000,10000,150000,190000,260000,330000,420000,510000,620000,740000,880000,1020000,1180000,1330000,1420000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,0,
+4,DUMMY 4,225000,0,,0,,0,,0,,4000,,22000,,46000,,76000,,111000,,147000,,184000,,219000,,249000,,274000,,290000,,297000,,302000,,307000,,307000,,305000,,295000,,280000,,260000,,240000,,230000,,225000,0,
diff --git a/windpowerlib/data/power_coefficient_curves.csv b/windpowerlib/data/power_coefficient_curves.csv
deleted file mode 100644
index 2d174057..00000000
--- a/windpowerlib/data/power_coefficient_curves.csv
+++ /dev/null
@@ -1,92 +0,0 @@
-,turbine_id,p_nom,0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7,7.5,8,8.5,9,9.5,10,10.5,11,11.5,12,12.5,13,13.5,14,14.5,15,15.5,16,16.5,17,17.5,18,18.5,19,19.5,20,20.5,21,21.5,22,22.5,23,23.5,24,24.5,25,25.5,26,source,modificationtimestamp
-0,AN BONUS 31,300000,0,,0,,0,,0,,0.132,,0.376,,0.461,,0.481,,0.467,,0.436,,0.397,,0.356,,0.311,,0.27,,0.229,,0.19,,0.16,,0.135,,0.114,,0.096,,0.08,,0.065,,0.053,,0.043,,0.036,,0.031,0,,k.A.,2013-11-21 11:51:06
-1,AN BONUS 44,600000,0,,0,,0,,0.155,,0.356,,0.423,,0.414,,0.409,,0.424,,0.414,,0.388,,0.35,,0.31,,0.268,,0.226,,0.19,,0.16,,0.133,,0.109,,0.089,,0.073,,0.061,,0.051,,0.044,,0.037,,0.031,0,,k.A.,2013-11-21 11:51:06
-2,AN BONUS 54,1000000,0,,0,,0,,0.076,,0.26,,0.387,,0.418,,0.434,,0.444,,0.44,,0.422,,0.388,,0.342,,0.296,,0.25,,0.208,,0.173,,0.144,,0.121,,0.103,,0.088,,0.076,,0.066,,0.058,,0.051,,0.045,0,,k.A.,2013-11-21 11:51:06
-3,AN BONUS 82,2300000,0,,0,,0,,0.088,,0.249,,0.362,,0.408,,0.425,,0.436,,0.443,,0.434,,0.407,,0.362,,0.306,,0.253,,0.208,,0.172,,0.143,,0.121,,0.103,,0.088,,0.076,,0.066,,0.058,,0.051,,0.045,0,,k.A.,2013-11-21 11:51:06
-4,NEG MICON NM 60 1000,1000000,0,,0,,0,,0.036,,0.331,,0.388,,0.38,,0.443,,0.45,,0.428,,0.383,,0.33,,0.292,,0.251,,0.215,,0.178,,0.147,,0.121,,0.1,,0.083,,0.069,0,0,,0,,0,,0,,0,,,k.A.,2013-11-21 11:51:07
-5,DEWIND D8 2000,2000000,0,,0,,0,,0,,0.259,,0.377,,0.415,,0.433,,0.439,,0.439,,0.429,,0.4,,0.348,,0.289,,0.236,,0.192,,0.159,,0.132,,0.111,,0.095,,0.081,,0.07,,0.061,,0.053,,0.047,,0.042,0,,k.A.,2013-11-21 11:51:06
-6,FUHRLÄNDER FL 54 1000,1000000,0,,0,,0,,0,,0.271,,0.368,,0.365,,0.409,,0.437,,0.444,,0.415,,0.367,,0.323,,0.289,,0.251,,0.213,,0.18,,0.149,,0.125,,0.105,,0.09,,0.077,,0.066,,0.057,,0.05,,0.044,0,,k.A.,2013-11-21 11:51:06
-7,FUHRLÄNDER MD 70,1500000,0,,0,,0,,0,,0.192,,0.332,,0.379,,0.376,,0.408,,0.415,,0.415,,0.401,,0.357,,0.289,,0.232,,0.189,,0.155,,0.13,,0.109,,0.093,,0.08,,0.069,,0.06,,0.052,,0.046,,0.041,0,,k.A.,2013-11-21 11:51:06
-8,FUHRLANDER FL 80 2500,2500000,0,,0,,0,,0,,0.145,,0.325,,0.394,,0.414,,0.422,,0.428,,0.429,,0.418,,0.39,,0.35,,0.294,,0.241,,0.198,,0.165,,0.139,,0.118,,0.102,,0.088,,0.076,,0.067,,0.059,,0.052,0,,k.A.,2013-11-21 11:51:06
-9,FUHRLANDER FL 90 2500,2500000,0,,0,,0,,0,,0.151,,0.329,,0.396,,0.418,,0.436,,0.444,,0.431,,0.408,,0.358,,0.292,,0.234,,0.19,,0.157,,0.131,,0.11,,0.094,,0.08,,0.069,,0.06,,0.053,,0.046,,0.041,0,,k.A.,2013-11-21 11:51:06
-10,GAMESA G 52 850,850000,0,,0,,0,,0,,0.335,,0.401,,0.438,,0.455,,0.461,,0.459,,0.434,,0.395,,0.347,,0.294,,0.238,,0.193,,0.16,,0.133,,0.112,,0.095,,0.082,,0.071,,0.061,,0.054,,0.047,,0.042,0.036,,k.A.,2013-11-21 11:51:07
-11,GAMESA G 80 2000,2000000,0,,0,,0,,0,,0.336,,0.395,,0.421,,0.433,,0.438,,0.436,,0.421,,0.39,,0.342,,0.286,,0.234,,0.192,,0.159,,0.132,,0.111,,0.095,,0.081,,0.07,,0.061,,0.053,,0.047,,0.042,0,,k.A.,2013-11-21 11:51:07
-12,GAMESA G 87 2000,2000000,0,,0,,0,,0,,0.339,,0.398,,0.426,,0.44,,0.446,,0.443,,0.42,,0.375,,0.312,,0.249,,0.2,,0.163,,0.134,,0.112,,0.094,,0.08,,0.069,,0.059,,0.052,,0.045,,0.04,,0.035,0,,k.A.,2013-11-21 11:51:07
-13,GAMESA G 90 2000,2000000,0,,0,,0,,0.2,,0.341,,0.404,,0.432,,0.445,,0.452,,0.449,,0.423,,0.366,,0.293,,0.233,,0.187,,0.152,,0.125,,0.104,,0.088,,0.075,,0.064,,0.055,,0.046,,0.035,,0.027,,0.02,0,,k.A.,2013-11-21 11:51:07
-14,"GE 1,5 S",1500000,0,,0,,0,,0,,0.183,,0.311,,0.381,,0.398,,0.426,,0.432,,0.427,,0.411,,0.357,,0.286,,0.229,,0.186,,0.153,,0.128,,0.108,,0.091,,0.078,,0.068,,0.059,,0.052,,0.045,,0.04,0,,k.A.,2013-11-21 11:51:07
-15,"GE 1,5 SLE",1500000,0,,0,,0,,0,0.164,0.236,0.319,0.367,0.39,0.406,0.416,0.425,0.433,0.438,0.448,0.444,0.434,0.414,0.389,0.358,0.323,0.291,0.263,0.236,0.212,0.191,0.173,0.156,0.141,0.128,0.117,0.107,0.098,0.09,0.083,0.077,0.071,0.066,0.061,0.057,0.053,0.049,0.046,0.043,0.041,0.038,0.036,0.034,0,,k.A.,2013-11-21 11:51:07
-16,"GE 2,3",2300000,0,,0,,0,,0.07,,0.261,,0.352,,0.395,,0.418,,0.429,,0.436,,0.419,,0.373,,0.308,,0.246,,0.197,,0.16,,0.132,,0.11,,0.093,,0.079,,0.068,,0.058,,0.051,,0.044,,0.039,,0.035,0,,k.A.,2013-11-21 11:51:07
-17,"GE 2,5 XL",2500000,0,,0,,0,,0.015,0.179,0.296,0.34,0.368,0.386,0.398,0.408,0.415,0.42,0.424,0.427,0.429,0.426,0.411,0.389,0.36,0.328,0.296,0.264,0.236,0.211,0.189,0.17,0.154,0.14,0.127,0.116,0.106,0.097,0.089,0.082,0.076,0.07,0.065,0.06,0.056,0.052,0.049,0.046,0.043,0.04,0.038,0.035,0.033,0,,k.A.,2013-11-21 11:51:07
-18,"GE 1,6",1600000,0,,0,,0,,0.008,0.078,0.263,0.372,0.431,0.472,0.485,0.487,0.49,0.485,0.471,0.444,0.407,0.368,0.327,0.286,0.251,0.221,0.195,0.172,0.153,0.137,0.123,0.11,0.1,0.09,0.082,0.075,0.069,0.063,0.058,0.053,0.049,0.045,0.042,0.039,0.036,0.034,0.032,0.03,0.028,0.026,0.024,0.023,0.022,0,,k.A.,2013-11-21 11:51:07
-19,"GE 2,75",2750000,0,,0,,0,,0.123,0.251,0.318,0.363,0.393,0.415,0.426,0.435,0.441,0.445,0.448,0.448,0.445,0.434,0.415,0.388,0.359,0.331,0.302,0.274,0.247,0.221,0.199,0.179,0.161,0.146,0.133,0.121,0.111,0.102,0.093,0.086,0.079,0.073,0.068,0.063,0.059,0.055,0.051,0.048,0.045,0.042,0.039,0.037,0.035,0,,k.A.,2013-11-21 11:51:07
-20,NEG MICON NM 72C 1500,1500000,0,,0,,0,,0.018,,0.328,,0.417,,0.426,,0.422,,0.453,,0.452,,0.425,,0.377,,0.324,,0.264,,0.214,,0.178,,0.147,,0.122,,0.103,,0.088,,0.075,,0.065,,0.056,,0.049,,0.044,,0.038,0,,k.A.,2013-11-21 11:51:07
-21,NORDEX N 62,1300000,0,,0,,0,,0,,0.169,,0.35,,0.398,,0.355,,0.407,,0.424,,0.411,,0.376,,0.33,,0.287,,0.246,,0.207,,0.172,,0.142,,0.117,,0.097,,0.081,,0.069,,0.059,,0.051,,0.045,,0.039,0,,k.A.,2013-11-21 11:51:08
-22,NORDEX S 70 1500,1500000,0,,0,,0,,0,,0.159,,0.295,,0.373,,0.407,,0.44,,0.428,,0.431,,0.409,,0.35,,0.29,,0.232,,0.189,,0.155,,0.13,,0.109,,0.093,,0.08,,0.069,,0.06,,0.052,,0.046,,0.041,0,,k.A.,2013-11-21 11:51:08
-23,NORDEX S 77 1500,1500000,0,,0,,0,,0,,0.241,,0.367,,0.396,,0.409,,0.411,,0.411,,0.39,,0.351,,0.299,,0.239,,0.192,,0.156,,0.128,,0.107,,0.09,,0.077,,0.066,,0.057,,0.049,,0.043,,0.038,,0.034,0,,k.A.,2013-11-21 11:51:08
-24,NORDEX N 43 600,600000,0,,0,,0,,0,,0.299,,0.405,,0.375,,0.406,,0.43,,0.427,,0.409,,0.375,,0.347,,0.299,,0.253,,0.206,,0.17,,0.142,,0.12,,0.1,,0.083,,0.072,,0.062,,0.054,,0.047,,0.041,0,,k.A.,2013-11-21 11:51:08
-25,NORDEX N 60 1300,1300000,0,,0,,0,,0,,0.262,,0.337,,0.35,,0.406,,0.424,,0.425,,0.407,,0.378,,0.34,,0.295,,0.262,,0.223,,0.189,,0.16,,0.131,,0.111,,0.095,,0.082,,0.071,,0.062,,0.054,,0.048,0,,k.A.,2013-11-21 11:51:08
-26,FRISIA F 48,750000,0,,0,,0,,0,,0.183,,0.328,,0.377,,0.395,,0.407,,0.416,,0.41,,0.397,,0.372,,0.308,,0.247,,0.2,,0.165,,0.138,,0.116,,0.099,,0.085,0,0,,0,,0,,0,,0,,,k.A.,2013-11-21 11:51:07
-27,NORDEX N 90 2500 HS,2500000,0,,0,,0,,0.01,0.174,0.305,0.383,0.425,0.45,0.463,0.471,0.475,0.479,0.48,0.479,0.476,0.47,0.462,0.451,0.43,0.4,0.364,0.328,0.292,0.261,0.234,0.21,0.19,0.172,0.157,0.143,0.131,0.12,0.11,0.101,0.094,0.087,0.08,0.074,0.069,0.065,0.06,0.056,0.053,0.049,0.046,0.044,0.041,0,,k.A.,2013-11-21 11:51:08
-28,NORDEX N 90 2500 LS,2500000,0,,0,,0,,0.01,0.221,0.337,0.4,0.435,0.454,0.465,0.471,0.475,0.478,0.477,0.473,0.465,0.455,0.442,0.427,0.409,0.385,0.355,0.323,0.291,0.261,0.234,0.21,0.19,0.172,0.157,0.143,0.131,0.12,0.11,0.101,0.094,0.087,0.08,0.074,0.069,0.065,0.06,0.056,0.053,0.049,0.046,0.044,0.041,0.037,0,k.A.,2013-11-21 11:51:08
-29,NORDEX N 100 2500,2500000,0,,0,,0,,0.023,0.224,0.329,0.392,0.427,0.447,0.456,0.462,0.465,0.468,0.468,0.466,0.462,0.457,0.44,0.411,0.376,0.339,0.302,0.267,0.237,0.212,0.19,0.171,0.155,0.14,0.127,0.116,0.106,0.097,0.089,0.082,0.076,0.07,0.065,0.061,0.056,0.053,0.049,0.046,0.043,0.04,0.038,0.035,0.033,0,,k.A.,2013-11-21 11:51:08
-30,NORDEX N 100 3300,3300000,0,,0,,0,,0,0.063,0.225,0.311,0.366,0.398,0.419,0.432,0.441,0.447,0.451,0.453,0.453,0.451,0.446,0.438,0.423,0.399,0.37,0.34,0.309,0.279,0.251,0.226,0.204,0.185,0.168,0.153,0.14,0.129,0.118,0.109,0.1,0.093,0.086,0.08,0.074,0.069,0.065,0.06,0.057,0.053,0.05,0.047,0.044,0,,k.A.,2013-11-21 11:51:08
-31,NORDTANK NTK 600,600000,0,,0,,0,,0,,0,,0.136,,0.317,,0.393,,0.422,,0.423,,0.392,,0.366,,0.334,,0.293,,0.244,,0.199,,0.162,,0.131,,0.108,,0.092,,0.08,,0.07,,0.062,,0.054,,0.048,0,0,,,k.A.,2013-11-21 11:51:08
-32,NORDEX N 117 3000,3000000,0,,0,,0,,0.09,0.199,0.307,0.37,0.406,0.428,0.44,0.448,0.453,0.456,0.457,0.453,0.445,0.43,0.403,0.369,0.334,0.298,0.265,0.234,0.208,0.186,0.167,0.15,0.135,0.123,0.112,0.102,0.093,0.085,0.078,0.072,0.067,0.062,0.057,0.053,0.049,0.046,0.043,0.04,0.038,0.035,0.033,0.031,0.029,0,,k.A.,2013-11-21 11:51:08
-33,NORDTANK NTK 500,500000,0,,0,,0,,0,,0,,0.214,,0.35,,0.408,,0.428,,0.428,,0.416,,0.396,,0.366,,0.328,,0.286,,0.244,,0.204,,0.167,,0.135,,0.11,,0.091,,0.078,,0.068,,0.06,,0.054,,0.049,0,,k.A.,2013-11-21 11:51:08
-34,REPOWER MM 82 2050,2050000,0,,0,,0,,0,,0.309,,0.393,,0.449,,0.461,,0.463,,0.465,,0.445,,0.395,,0.342,,0.281,,0.23,,0.188,,0.155,,0.129,,0.109,,0.092,,0.079,,0.068,,0.06,,0.052,,0.046,,0.041,0,,k.A.,2013-11-21 11:51:08
-35,REPOWER 3.0 M122,3000000,0,,0,,0,,0.186,,0.358,,0.415,,0.442,,0.46,,0.453,,0.43,,0.387,,0.312,,0.275,,0.242,,0.191,,0.153,,0.124,,0.102,,0.085,,0.072,,0.061,,0.052,,0.045,,0.039,0,0,,0,,,k.A.,2013-11-21 11:51:09
-36,REPOWER 3.4 M104,3400000,0,,0,,0,,0,0.166,0.282,,0.389,,0.426,,0.443,,0.458,,0.46,,0.444,,0.413,,0.366,,0.295,,0.238,,0.194,,0.16,,0.133,,0.112,,0.095,,0.082,,0.071,,0.061,,0.054,,0.047,,0.042,0,,k.A.,2013-11-21 11:51:09
-37,REPOWER 5M,5000000,0,,0,,0,,0,0.162,0.258,,0.369,,0.393,,0.413,,0.419,,0.419,,0.415,,0.395,,0.36,,0.298,,0.239,,0.194,,0.16,,0.133,,0.112,,0.095,,0.082,,0.071,,0.061,,0.054,,0.047,,0.042,,0.041,k.A.,2013-11-21 11:51:09
-38,SIEMENS SWT 2.3 93,2300000,0,,0,,0,,0,,0.371,,0.411,,0.432,,0.442,,0.446,,0.449,,0.445,,0.405,,0.322,,0.254,,0.203,,0.165,,0.136,,0.113,,0.096,,0.081,,0.07,,0.06,,0.052,,0.046,,0.04,,0.036,0,,k.A.,2013-11-21 11:51:09
-39,SIEMENS SWT 3.6 120,3600000,0,,0,,0,,0,,0.363,,0.405,,0.424,,0.432,,0.435,,0.436,,0.42,,0.369,,0.298,,0.236,,0.189,,0.154,,0.127,,0.106,,0.089,,0.076,,0.065,,0.056,,0.049,,0.043,,0.038,,0.033,0,,k.A.,2013-11-21 11:51:09
-40,TACKE TW 600,600000,0,,0,,0,,0.108,,0.351,,0.42,,0.41,,0.432,,0.431,,0.413,,0.385,,0.351,,0.311,,0.271,,0.235,,0.203,,0.172,,0.144,,0.121,,0.103,,0.088,,0.075,,0.067,,0.055,,0.049,,0.043,0,,k.A.,2013-11-21 11:51:09
-41,VENSYS 100,2500000,0,,0,,0,,0.365,,0.409,,0.418,,0.423,,0.424,,0.422,,0.415,,0.397,,0.358,,0.299,,0.237,,0.19,,0.155,,0.127,,0.106,,0.089,,0.076,,0.065,,0.056,,0.049,,0.043,,0.038,,0.033,0,,k.A.,2013-11-21 11:51:09
-42,VENSYS 77,1500000,0,,0,,0,,0.238,0.339,0.388,0.406,0.414,0.42,0.426,0.427,0.43,0.432,0.434,0.433,0.429,0.422,0.407,0.39,0.362,0.332,0.298,0.268,0.239,0.214,0.1919999523,0.1728149147,0.1561030724,0.141478398,0.1286249681,0.1172825487,0.107235471,0.0983039756,0.0903374261,0.0832089502,0.0768111779,0.0710528322,0.0658559836,0.0611538276,0.0568888748,0.0530114701,0.0494785752,0.0462527622,0.0433013783,0.0405958502,0.0381111016,0.035825064,0.034,0,,k.A.,2013-11-21 11:51:09
-43,VENSYS 109,2500000,0,,0,,0,,0.355,0.398,0.414,0.416,0.418,0.42,0.421,0.421,0.42,0.419,0.419,0.417,0.414,0.402,0.384,0.357,0.322,0.286,0.253,0.224,0.1994492351,0.1780988546,0.1596902221,0.1437336404,0.129834065,0.1176704292,0.106979973,0.0975462547,0.0891898981,0.0817613937,0.0751354543,0.0692065575,0.0638854016,0.0590960697,0.0547737462,0.0508628684,0.0473156214,0.0440907059,0.0411523262,0.0384693526,0.0360146272,0.0337643851,0.0316977698,0.0297964263,0.028044158,0,,k.A.,2013-11-21 11:51:09
-44,VENSYS 112,2500000,0,,0,,0,,0.291,0.362,0.399,0.415,0.419,0.42,0.421,0.421,0.421,0.42,0.418,0.415,0.411,0.4,0.376,0.345,0.311,0.272,0.24,0.2120333609,0.1884968858,0.1683189181,0.1509211582,0.1358407997,0.1227044913,0.1112087968,0.1011053853,0.0921897004,0.0842922162,0.077271633,0.0710095436,0.0654062201,0.0603772646,0.0558509291,0.0517659573,0.0480698374,0.0447173802,0.0416695544,0.0388925299,0.0363568863,0.0340369572,0.0319102825,0.0299571512,0.0281602161,0.0265041701,0,,k.A.,2013-11-21 11:51:09
-45,VESTAS V 42 600,600000,0,,0,,0,,0,,0,,0.207,,0.355,,0.412,,0.433,,0.433,,0.42,,0.39,,0.348,,0.298,,0.25,,0.207,,0.172,,0.144,,0.121,,0.103,,0.088,,0.076,,0.066,,0.058,,0.051,,0.045,0,,k.A.,2013-11-21 11:51:09
-46,VESTAS V 47 660,660000,0,,0,,0,,0,,0.078,,0.338,,0.416,,0.442,,0.445,,0.431,,0.401,,0.361,,0.314,,0.266,,0.221,,0.182,,0.151,,0.126,,0.106,,0.091,,0.078,,0.067,,0.058,,0.051,,0.045,,0.04,0,,k.A.,2013-11-21 11:51:09
-47,NORDEX N 117 2400,2400000,0,,0,,0,,0.141,0.29,0.365,0.407,0.43,0.444,0.452,0.457,0.46,0.458,0.452,0.444,0.424,0.392,0.353,0.313,0.274,0.24,0.211,0.187,0.166,0.148,0.133,0.12,0.108,0.098,0.089,0.081,0.074,0.068,0.062,0.058,0.053,0.049,0.046,0,0,,0,,0,,0,,0,,,k.A.,2013-11-21 11:51:08
-48,VESTAS V 66 1650,1650000,0,,0,,0,,0,,0.104,,0.309,,0.373,,0.402,,0.418,,0.422,,0.409,,0.383,,0.349,,0.311,,0.27,,0.229,,0.191,,0.16,,0.135,,0.115,,0.098,,0.085,,0.074,,0.065,,0.057,,0.05,0,,k.A.,2013-11-21 11:51:09
-49,VESTAS V 66 1750,1750000,0,,0,,0,,0,,0.248,,0.358,,0.393,,0.409,,0.418,,0.418,,0.406,,0.382,,0.354,,0.322,,0.284,,0.242,,0.203,,0.17,,0.143,,0.1215755666,0,0.1042358514,0,0.0900428476,0,0.078313938,0,0.0685367643,0,0.0603216733,0,0.0533687559,0,,k.A.,2013-11-21 11:51:10
-50,VESTAS V 52 850,850000,0,,0,,0,,0,,0.306,,0.415,,0.445,,0.455,,0.456,,0.448,,0.426,,0.388,,0.338,,0.284,,0.234,,0.193,,0.159,,0.133,,0.112,,0.095,,0.082,,0.071,,0.061,,0.054,,0.047,0,0.042,,,k.A.,2013-11-21 11:51:09
-51,REPOWER 3.2 M114,3200000,0,,0,,0,,0.16,,0.377,,0.418,,0.443,,0.456,,0.46,,0.446,,0.415,,0.364,,0.296,,0.233,,0.187,,0.152,,0.125,,0.104,,0.088,,0.075,,0.064,,0.055,,0.048,0,0,,0,,0,,,k.A.,2013-11-21 11:51:09
-52,ENERCON E 70 2300,2300000,0,,0,,0.103,,0.275,,0.361,,0.419,,0.458,,0.481,,0.504,,0.505,,0.504,,0.493,,0.453,,0.39,,0.335,,0.281,,0.233,,0.194,,0.163,,0.139,,0.119,,0.103,,0.089,,0.078,,0.069,,0.061,0,,k.A.,2013-11-21 11:51:11
-53,AN BONUS 62,1300000,0,,0,,0,,0.098,,0.254,,0.387,,0.428,,0.46,,0.466,,0.45,,0.418,,0.379,,0.337,,0.292,,0.247,,0.205,,0.171,,0.143,,0.12,,0.102,,0.088,,0.076,,0.066,,0.058,,0.051,,0.045,0,,k.A.,2013-11-21 11:51:06
-54,FUHRLÄNDER FL 100 2500,2500000,0,,0,,0,,0,,0.268,,0.408,,0.436,,0.454,,0.464,,0.47,,0.45,,0.385,,0.304,,0.24,,0.192,,0.156,,0.129,,0.107,,0.09,,0.077,,0.066,,0.057,,0.05,,0.043,,0.038,,0.034,0,,http://www.windenergie-im-binnenland.de,2013-11-21 11:51:06
-55,NORDEX N 90 2300,2300000,0,,0,,0,,0,,0.14,,0.359,,0.418,,0.434,,0.436,,0.435,,0.417,,0.388,,0.331,,0.269,,0.215,,0.175,,0.144,,0.12,,0.101,,0.086,,0.074,,0.064,,0.055,,0.049,,0.043,,0.038,0,,k.A.,2013-11-21 11:51:08
-56,NORDEX N 80 2500,2500000,0,,0,,0,,0.012,0.182,0.305,0.374,0.416,0.439,0.454,0.464,0.47,0.473,0.475,0.474,0.47,0.463,0.453,0.441,0.428,0.412,0.395,0.372,0.346,0.319,0.292,0.266,0.241,0.218,0.198,0.181,0.165,0.152,0.139,0.128,0.118,0.11,0.102,0.094,0.088,0.082,0.076,0.071,0.067,0.063,0.059,0.055,0.052,0,,k.A.,2013-11-21 11:51:08
-57,VESTAS V 80 2000,2000000,0,,0,,0,,0,,0.335,,0.405,,0.429,,0.442,,0.448,,0.446,,0.432,,0.403,,0.356,,0.293,,0.237,,0.192,,0.159,,0.132,,0.111,,0.095,,0.081,,0.07,,0.061,,0.053,,0.047,,0.042,0,,k.A.,2013-11-21 11:51:10
-58,VESTAS V 90 1800,1800000,0,,0,,0,,0,,0.353,,0.419,,0.441,,0.45,,0.452,,0.438,,0.403,,0.339,,0.266,,0.21,,0.168,,0.137,,0.113,,0.094,,0.079,,0.067,,0.058,,0.05,,0.043,,0.038,,0.033,,0.03,0,,k.A.,2013-11-21 11:51:10
-59,VESTAS V 90 2000,2000000,0,,0,,0,,0,,0.353,,0.421,,0.441,,0.45,,0.452,,0.438,,0.408,,0.362,,0.294,,0.234,,0.187,,0.152,,0.125,,0.104,,0.088,,0.075,,0.064,,0.055,,0.048,,0.042,,0.037,,0.033,0,,k.A.,2013-11-21 11:51:10
-60,VESTAS V 90 3000,3000000,0,,0,,0,,0,,0.309,,0.39,,0.419,,0.435,,0.444,,0.448,,0.439,,0.414,,0.378,,0.331,,0.277,,0.228,,0.188,,0.157,,0.132,,0.112,,0.096,,0.083,,0.072,,0.063,,0.056,,0.049,0,,k.A.,2013-11-21 11:51:10
-61,VESTAS V 112 3000,3000000,0,,0,,0,,0.141,0.263,0.337,0.375,0.399,0.416,0.427,0.434,0.441,0.444,0.446,0.446,0.444,0.439,0.426,0.402,0.372,0.332,0.294,0.261,0.232,0.207,0.186,0.167,0.151,0.137,0.124,0.113,0.104,0.095,0.087,0.08,0.074,0.069,0.064,0.059,0.055,0.051,0.048,0.045,0.042,0.039,0.037,0.035,0.033,0,,k.A.,2013-11-21 11:51:10
-62,VESTAS V 117 3300,3300000,0,,0,,0,,0.163,0.287,0.346,0.377,0.397,0.413,0.422,0.429,0.434,0.438,0.44,0.44,0.438,0.432,0.418,0.393,0.36,0.324,0.288,0.256,0.228,0.204,0.183,0.164,0.148,0.135,0.122,0.112,0.102,0.094,0.086,0.079,0.073,0.068,0.063,0.058,0.054,0.05,0.047,0.044,0.041,0.039,0.036,0.034,0.032,0,,k.A.,2013-11-21 11:51:10
-63,VESTAS NM 52 900,900000,0,,0,,0,,0,,0.324,,0.418,,0.42,,0.446,,0.456,,0.444,,0.416,,0.37,,0.323,,0.277,,0.235,,0.199,,0.167,,0.141,,0.118,,0.1,,0.085,,0.072,,0.062,,0.054,,0.047,,0.041,0,,k.A.,2013-11-21 11:51:10
-64,ENERCON E 101 3000,3000000,0,,0,,0.076,,0.279,,0.376,,0.421,,0.452,,0.469,,0.478,,0.478,,0.477,,0.439,,0.358,,0.283,,0.227,,0.184,,0.152,,0.127,,0.107,,0.091,,0.078,,0.067,,0.058,,0.051,,0.045,,0.04,0,,k.A.,2013-11-21 11:51:10
-65,ENERCON E 126 7500,7500000,0,,0,,0,,0.191,,0.352,,0.423,,0.453,,0.47,,0.478,,0.477,,0.483,,0.47,,0.429,,0.381,,0.329,,0.281,,0.236,,0.199,,0.168,,0.142,,0.122,,0.105,,0.092,,0.08,,0.071,,0.063,0,,k.A.,2013-11-21 11:51:10
-66,ENERCON E 115 2500,2500000,0,,0,,0.058,,0.276,,0.371,,0.416,,0.446,,0.463,,0.464,,0.429,,0.365,,0.289,,0.225,,0.177,,0.141,,0.115,,0.095,,0.079,,0.067,,0.057,,0.049,,0.042,,0.036,,0.032,,0.028,,0.025,0,,k.A.,2013-11-21 11:51:11
-67,ENERCON E 48 800,800000,0,,0,,0.226,,0.401,,0.451,,0.476,,0.501,,0.502,,0.5,,0.501,,0.501,,0.455,,0.392,,0.324,,0.266,,0.217,,0.178,,0.149,,0.125,,0.107,,0.091,,0.079,,0.069,,0.06,,0.053,,0.047,0,,k.A.,2013-11-21 11:51:10
-68,ENERCON E 82 2000,2000000,0,,0,,0.116,,0.286,,0.396,,0.43,,0.459,,0.48,,0.492,,0.5,,0.498,,0.439,,0.358,,0.288,,0.231,,0.188,,0.155,,0.129,,0.109,,0.092,,0.079,,0.068,,0.06,,0.052,,0.046,,0.041,0,,k.A.,2013-11-21 11:51:11
-69,ENERCON E 53 800,800000,0,,0,,0.185,,0.384,,0.439,,0.456,,0.483,,0.492,,0.486,,0.487,,0.477,,0.414,,0.334,,0.273,,0.218,,0.178,,0.146,,0.122,,0.103,,0.087,,0.075,,0.065,,0.056,,0.049,,0.043,,0.038,0,,k.A.,2013-11-21 11:51:10
-70,ENERCON E 58 1000,1000000,0,,0,,0,,0.108,,0.276,,0.357,,0.401,,0.421,,0.46,,0.463,,0.448,,0.42,,0.353,,0.281,,0.225,,0.183,,0.151,,0.126,,0.106,,0.09,,0.077,,0.067,,0.058,,0.051,,0.045,,0.04,0,,k.A.,2013-11-21 11:51:10
-71,ENERCON E 70 2000,2000000,0,,0,,0.103,,0.275,,0.361,,0.419,,0.458,,0.481,,0.504,,0.505,,0.504,,0.493,,0.437,,0.366,,0.308,,0.25,,0.206,,0.172,,0.145,,0.123,,0.106,,0.091,,0.079,,0.069,,0.061,,0.054,0,,k.A.,2013-11-21 11:51:11
-72,ENERCON E 82 2300,2300000,0,,0,,0.116,,0.286,,0.396,,0.43,,0.459,,0.48,,0.492,,0.5,,0.488,,0.439,,0.376,,0.317,,0.265,,0.215,,0.177,,0.148,,0.125,,0.106,,0.091,,0.078,,0.068,,0.06,,0.053,,0.046,0,,k.A.,2013-11-21 11:51:11
-73,ENERCON E 82 3000,3000000,0,,0,,0.116,,0.286,,0.396,,0.43,,0.459,,0.48,,0.492,,0.5,,0.488,,0.441,,0.394,,0.349,,0.304,,0.261,,0.223,,0.19,,0.16,,0.136,,0.117,,0.101,,0.088,,0.077,,0.068,,0.06,0,,k.A.,2013-11-21 11:51:11
-74,ENERCON E 92 2300,2300000,0,,0,,0.111,,0.272,,0.377,,0.409,,0.437,,0.456,,0.468,,0.473,,0.446,,0.385,,0.318,,0.257,,0.21,,0.171,,0.141,,0.117,,0.099,,0.084,,0.072,,0.062,,0.054,,0.047,,0.042,,0.037,0,,k.A.,2013-11-21 11:51:11
-75,ENERCON E 112 4500,4500000,0,,0,,0,,0.225,,0.337,,0.385,,0.415,,0.435,,0.435,,0.435,,0.435,,0.425,,0.387,,0.324,,0.262,,0.213,,0.176,,0.147,,0.123,,0.105,,0.09,,0.078,,0.068,,0.059,,0.052,,0.046,0,,k.A.,2013-11-21 11:51:11
-76,DEWIND D4 600,600000,0,,0,,0,,0.234,,0.31,,0.375,,0.388,,0.416,,0.43,,0.438,,0.441,,0.4,,0.313,,0.246,,0.197,,0.16,,0.132,,0.11,,0.093,,0.079,0,0,,0,,0,,0,,0,,0,,,k.A.,2013-11-21 11:51:06
-77,DEWIND D6 1000,1000000,0,,0,,0,,0.24,,0.287,,0.346,,0.396,,0.427,,0.436,,0.435,,0.422,,0.395,,0.313,,0.246,,0.197,,0.16,,0.132,,0.103,,0.078,,0.059,,0.044,,0.033,,0.024,,0.017,0,0,,0,,,k.A.,2013-11-21 11:51:06
-78,FRISIA F 56,850000,0,,0,,0,,0,,0.237,,0.356,,0.374,,0.413,,0.423,,0.424,,0.417,,0.414,,0.326,,0.256,,0.205,,0.167,,0.138,,0.115,,0.097,,0.082,,0.07,0,0,,0,,0,,0,,0,,,k.A.,2013-11-21 11:51:07
-79,FUHRLÄNDER MD 77,1500000,0,,0,,0,,0.016,,0.206,,0.345,,0.38,,0.402,,0.411,,0.409,,0.398,,0.383,,0.304,,0.239,,0.192,,0.156,,0.128,,0.107,,0.09,,0.077,,0.066,0,0,,0,,0,,0,,0,,,k.A.,2013-11-21 11:51:06
-80,GAMESA G 58,850000,0,,0,,0,,0.222,,0.301,,0.388,,0.424,,0.437,,0.445,,0.445,,0.429,,0.371,,0.302,,0.239,,0.191,,0.156,,0.128,,0.107,,0.09,,0.077,,0.066,,0.057,,0.048,,0.04,0,0,,0,,,k.A.,2013-11-21 11:51:07
-81,"GE 1,5 XLE",1500000,0,,0,,0,,0.034,0.185,0.286,0.349,0.386,0.402,0.416,0.425,0.433,0.439,0.441,0.443,0.433,0.419,0.394,0.364,0.331,0.296,0.264,0.234,0.209,0.186,0.167,0.15,0.136,0.123,0.112,0.102,0.093,0.085,0.079,0.072,0.067,0.062,0.057,0,0,,0,,0,,0,,0,,,k.A.,2013-11-21 11:51:07
-82,NEG MICON NM 82 1500,1500000,0,,0,,0,,0,,0.333,,0.428,,0.439,,0.46,,0.454,,0.425,,0.39,,0.333,,0.267,,0.211,,0.169,,0.137,,0.113,,0.094,,0.08,,0,,0,,0,,0,,0,,0,,0,,,k.A.,2013-11-21 11:51:07
-83,NEG MICON NM 82 1650,1650000,0,,0,,0,,0,,0.135,,0.356,,0.442,,0.461,,0.458,,0.431,,0.397,,0.349,,0.293,,0.232,,0.186,,0.151,,0.125,,0.104,,0.087,,0.074,,0.064,0,0,,0,,0,,0,,0,,,k.A.,2013-11-21 11:51:08
-84,REPOWER MM 100 2000,2000000,0,,0,,0,,0.154,,0.331,,0.397,,0.435,,0.452,,0.457,,0.445,,0.395,,0.312,,0.241,,0.189,,0.152,,0.123,,0.102,,0.085,,0.071,,0.061,,0.052,,0.045,,0.039,0,0,,0,,0,,,k.A.,2013-11-21 11:51:09
-85,REPOWER MM 92 2050,2050000,0,,0,,0,,0.18,,0.357,,0.398,,0.44,,0.457,,0.465,,0.458,,0.436,,0.365,,0.287,,0.227,,0.182,,0.148,,0.122,,0.101,,0.085,,0.073,,0.062,,0.054,,0.047,,0.041,,0.036,,0,,,k.A.,2013-11-21 11:51:08
-86,VENSYS 82,1500000,0,,0,,0,,0.297,0.378,0.414,0.426,0.433,0.439,0.445,0.446,0.448,0.45,0.451,0.448,0.441,0.427,0.405,0.375,0.339,0.302,0.267,0.237,0.2114505974,0.1888155109,0.1692991846,0.1523824552,0.1376465074,0.124750955,0.1134172272,0.1034158583,0.0945566787,0.0866811825,0.0796565437,0.0733708902,0.067729547,0.0626520289,0.0580696203,0.0539234152,0.0501627214,0.0467437546,0.0436285652,0,0,,0,,0,,,k.A.,2013-11-21 11:51:09
-87,VESTAS V 100 1800,1800000,0,,0,,0,,0.1,0.252,0.351,0.404,0.426,0.437,0.441,0.446,0.451,0.451,0.449,0.444,0.431,0.403,0.364,0.32,0.28,0.246,0.217,,0.17,,0.136,,0.111,,0.091,,0.076,,0.064,,0.055,,0.047,,0,,0,,0,,0,,0,,,k.A.,2013-11-21 11:51:10
-88,VESTAS V 126 3300,3300000,0,,0,,0,,0.175,0.302,0.362,0.394,0.412,0.423,0.432,0.438,0.443,0.446,0.447,0.444,0.435,0.419,0.394,0.36,0.321,0.284,0.25,0.221,0.197,0.176,0.157,0.142,0.128,0.116,0.105,0.096,0.088,0.081,0.074,0.068,0.063,0.058,0.054,0.05,0.047,0.043,0.041,0.038,0,,0,,0,,,k.A.,2013-11-21 11:51:10
-89,VESTAS V 44 600,600000,0,,0,,0,,0,,0,,0.258,,0.383,,0.423,,0.432,,0.423,,0.398,,0.363,,0.319,,0.273,,0.228,,0.189,,0.157,,0.131,,0.11,,0.094,,0.081,0,0,,0,,0,,0,,0,,,k.A.,2013-11-21 11:51:09
-90,VESTAS V 82 1650,1650000,0,,0,,0,,0,,0.135,,0.356,,0.442,,0.461,,0.458,,0.431,,0.397,,0.349,,0.293,,0.232,,0.186,,0.151,,0.125,,0.104,,0.087,,0.074,,0.064,0,0,,0,,0,,0,,0,,,k.A.,2013-11-21 11:51:10
diff --git a/windpowerlib/data/power_curves.csv b/windpowerlib/data/power_curves.csv
deleted file mode 100644
index c90968c9..00000000
--- a/windpowerlib/data/power_curves.csv
+++ /dev/null
@@ -1,161 +0,0 @@
-,turbine_id,p_nom,0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7,7.5,8,8.5,9,9.5,10,10.5,11,11.5,12,12.5,13,13.5,14,14.5,15,15.5,16,16.5,17,17.5,18,18.5,19,19.5,20,20.5,21,21.5,22,22.5,23,23.5,24,24.5,25,25.5,26,source,modificationtimestamp
-0,AAER A 70 1500,1500000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,17000.0,34000.0,66500.0,99000.0,146000.0,193000.0,261000.0,329000.0,418000.0,507000.0,623000.0,739000.0,880000.0,1021000.0,1177500.0,1334000.0,1417000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-1,AAER A 77 1500,1500000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,20000.0,40000.0,79000.0,118000.0,175000.0,232000.0,313500.0,395000.0,501500.0,608000.0,747500.0,887000.0,1056000.0,1225000.0,1337500.0,1450000.0,1475000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,,0.0,,0.0,,0.0,,0.0,,0.0,,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-2,AMERICAS WIND ENERGY AWE 52 900,900000,0.0,0.0,0.0,0.0,1000.0,4062.5,8000.0,18062.5,31000.0,47625.0,69000.0,93062.5,124000.0,156813.0,199000.0,241875.0,297000.0,349500.0,417000.0,474750.0,549000.0,622500.0,717000.0,742375.0,775000.0,796875.0,825000.0,846875.0,875000.0,885938.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-3,AMERICAS WIND ENERGY AWE 54 900,900000,0.0,0.0,0.0,0.0,1000.0,4500.0,9000.0,19500.0,33000.0,50500.0,73000.0,99250.0,133000.0,168000.0,213000.0,258938.0,318000.0,374438.00000000006,447000.0,508250.0,587000.0,665750.0,767000.0,794125.0,829000.0,852625.0,883000.0,890438.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,900000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-4,AN BONUS 31,300000,0.0,,0.0,,0.0,,0.0,,3900.0,,21700.0,,46000.0,,76300.0,,110600.0,,146900.0,,183700.0,,219200.0,,248500.0,,273800.0,,290400.0,,296900.0,,302300.0,,307000.0,,307300.0,,305000.0,,295000.0,,280000.0,,260000.0,,240000.0,,230000.0,,225000.0,0.0,,k.A.,21.11.13 11:43
-5,AN BONUS 44,600000,0.0,,0.0,,0.0,,3900.0,,21200.0,,49300.0,,83200.0,,130699.99999999999,,202000.0,,280800.0,,361600.0,,433700.0,,498600.0,,548100.0,,577300.0,,596000.0,,610000.0,,606900.0,,593400.0,,571300.0,,545600.0,,524700.0,,510000.0,,500700.0,,478700.0,,457700.0,0.0,,k.A.,21.11.13 11:43
-6,AN BONUS 54,1000000,0.0,,0.0,,0.0,,0.0,,24100.0,,69300.0,,130000.0,,219100.0,,333500.0,,463100.0,,598100.0,,730000.0,,846500.0,,928800.0,,972600.0,,990800.0,,997200.0,,999200.0,,999800.0,,999900.0,,1000000.0,,1000000.0,,1000000.0,,1000000.0,,1000000.0,,1000000.0,0.0,,http://www.inl.gov/wind/software/,21.11.13 11:43
-7,AN BONUS 62,1300000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,32000.0,57375.0,90000.0,125875.0,172000.0,221875.0,286000.0,353813.00000000006,441000.0,521500.0,625000.0,710313.0,820000.0,901375.0,1006000.0,1070310.0,1153000.0,1191500.0,1241000.0,1258060.0,1280000.0,1286130.0,1294000.0,1295750.0,1298000.0,1298880.0,1300000.0,1300000.0,1300000.0,1300000.0,1300000.0,1300000.0,1300000.0,1300000.0,1300000.0,1300000.0,1300000.0,1300000.0,1300000.0,1300000.0,1300000.0,1300000.0,1300000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-8,AN BONUS 76,2000000,0.0,,0.0,,0.0,,0.0,,43000.0,,133000.0,,237000.0,,401000.0,,623000.0,,886000.0,,1190000.0,,1502000.0,,1740000.0,,1891000.0,,1962000.0,,1988000.0,,1996000.0,,1999000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,0.0,,http://www.inl.gov/wind/software/,21.11.13 11:43
-9,AN BONUS 82,2300000,0.0,,0.0,,0.0,,0.0,,52000.0,,148000.0,,288000.0,,476000.0,,729000.0,,1055000.0,,1419000.0,,1769000.0,,2041000.0,,2198000.0,,2267000.0,,2291000.0,,2298000.0,,2300000.0,,2300000.0,,2300000.0,,2300000.0,,2300000.0,,2300000.0,,2300000.0,,2300000.0,,2300000.0,0.0,,http://www.inl.gov/wind/software/,21.11.13 11:43
-10,AREVA MULTIBRID M5000,5000000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,50000.0,165000.0,280000.0,410000.0,540000.0,705000.0,870000.0,1102500.0,1335000.0,1630000.0,1925000.0,2270000.0,2615000.0,3115000.0,3615000.0,4205000.0,4795000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-11,BARD VM 5.0,5000000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,30000.0,60000.0,147500.0,235000.0,382500.0,530000.0,735000.0,940000.0,1205000.0,1470000.0,1882500.0,2295000.0,2692500.0,3090000.0,3545000.0,4000000.0,4440000.0,4880000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-12,BWU 43,600000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-13,BWU 48,600000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-14,BWU 57,1050000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-15,Clipper C 89,2500000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14890.0,53340.0,96540.0,149370.0,231140.0,312920.0,423620.0,529520.0,664350.0,808830.0,987060.0,1190000.0,1380000.0,1610000.0,1840000.0,2010000.0,2160000.0,2280000.0,2380000.0,2445000.0,2480000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-16,Clipper C 93,2500000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,24560.0,58170.0,111010.0,173490.0,255260.0,356320.0,462220.0,572930.0,717400.0,905290.0,1050000.0,1270000.0,1490000.0,1710000.0,1920000.0,2100000.0,2250000.0,2350000.0,2440000.0,2470000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-17,Clipper C 96,2500000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,24620.0,67810.0,120650.0,178310.0,264900.0,365970.0,481510.0,621160.0,760810.0,982450.0,1130000.0,1380000.0,1590000.0,1840000.0,2020000.0,2180000.0,2320000.0,2410000.0,2450000.0,2480000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-18,Clipper C 99,2500000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,24620.0,72640.0,135120.0,187960.0,289020.0,390080.0,510450.0,654920.0,809050.0,987270.0,1210000.0,1440000.0,1660000.0,1880000.0,2100000.0,2240000.0,2350000.0,2420000.0,2460000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-19,# COMPOSITE IEC CLASS I,1500000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,28500.0,65250.0,102000.0,156000.0,210000.0,282750.0,355500.0,452250.0,549000.0,666750.0,784500.0,918750.0,1053000.0,1168500.0,1284000.0,1359000.0,1434000.0,1457250.0,1480500.0,1485750.0,1491000.0,1494000.0,1497000.0,1497750.0,1498500.0,1499160.0,1499530.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-20,# COMPOSITE IEC CLASS II,1500000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,42000.0,87000.0,132000.0,196500.0,261000.0,345750.0,430500.0,540750.0,651000.0,780750.0,910500.0,1038000.0,1165500.0,1261500.0,1357500.0,1407750.0,1458000.0,1473750.0,1489500.0,1493250.0,1497000.0,1498500.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-21,# COMPOSITE IEC CLASS III,1500000,0.0,0.0,0.0,0.0,0.0,0.0,9000.0,35250.0,61500.0,107250.0,153000.0,218250.0,283500.0,375000.0,466500.0,587250.0,708000.0,851250.0,994500.0,1125750.0,1257000.0,1338000.0,1419000.0,1449750.0,1480500.0,1488750.0,1497000.0,1497750.0,1498500.0,1499250.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,,0.0,,0.0,,0.0,,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-22,DEWIND D4 600,600000,0.0,,0.0,,0.0,,7000.0,,22000.0,,52000.0,,93000.0,,158000.0,,244000.0,,354000.0,,489000.0,,590000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,0.0,,0.0,,0.0,,0.0,,0.0,,0.0,,,http://www.inl.gov/wind/software/,21.11.13 11:43
-23,DEWIND D6 1000,1000000,0.0,,0.0,,0.0,,12000.0,,34000.0,,80000.0,,158000.0,,271000.0,,413000.0,,586000.0,,781000.0,,971000.0,,1000000.0,,1000000.0,,1000000.0,,1000000.0,,1000000.0,,940000.0,,840000.0,,750000.0,,650000.0,,560000.0,,475000.0,,390000.0,0.0,0.0,,0.0,,,k.A.,21.11.13 11:43
-24,DEWIND D8 2000,2000000,0.0,0.0,0.0,0.0,0.0,0.0,3626.6699999999996,10880.0,26750.0,50480.0,85970.0,141070.0,215770.0,298320.0,396560.0,518320.00000000006,655780.0,804990.0,965970.0,1150000.0,1350000.0,1540000.0,1700000.0,1810000.0,1900000.0,1950000.0,1990000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-25,ENERCON E 101 3000,3000000,0.0,,0.0,,3000.0,,37000.0,,118000.0,,258000.0,,479000.0,,790000.0,,1200000.0,,1710000.0,,2340000.0,,2867000.0,,3034000.0,,3050000.0,,3050000.0,,3050000.0,,3050000.0,,3050000.0,,3050000.0,,3050000.0,,3050000.0,,3050000.0,,3050000.0,,3050000.0,,3050000.0,,3050000.0,0.0,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-26,ENERCON E 112 4500,4500000,0.0,,0.0,,0.0,,38000.0,,135000.0,,301000.0,,561000.0,,933000.0,,1393000.0,,1983000.0,,2720000.0,,3540000.0,,4180000.0,,4450000.0,,4500000.0,,4500000.0,,4500000.0,,4500000.0,,4500000.0,,4500000.0,,4500000.0,,4500000.0,,4500000.0,,4500000.0,,4500000.0,,4500000.0,0.0,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-27,ENERCON E 115 2500,2500000,0.0,,0.0,,3000.0,,48000.0,,153000.0,,335000.0,,620000.0,,1023000.0,,1530000.0,,2015000.0,,2350000.0,,2480000.0,,2500000.0,,2500000.0,,2500000.0,,2500000.0,,2500000.0,,2500000.0,,2500000.0,,2500000.0,,2500000.0,,2500000.0,,2500000.0,,2500000.0,,2500000.0,,2500000.0,0.0,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-28,ENERCON E 126 7500,7500000,0.0,,0.0,,0.0,,55000.0,,175000.0,,410000.0,,760000.0,,1250000.0,,1900000.0,,2700000.0,,3750000.0,,4850000.0,,5750000.0,,6500000.0,,7000000.0,,7350000.0,,7500000.0,,7580000.0,,7580000.0,,7580000.0,,7580000.0,,7580000.0,,7580000.0,,7580000.0,,7580000.0,,7580000.0,0.0,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-29,ENERCON E 30,300000,0.0,,0.0,,0.0,,3700.0,,10000.0,,21600.0,,38300.0,,62400.0,,93100.0,,132600.0,,181600.0,,230500.0,,269300.0,,290500.0,,300000.0,,300000.0,,300000.0,,300000.0,,300000.0,,300000.0,,300000.0,,300000.0,,300000.0,,300000.0,,300000.0,,300000.0,0.0,,k.A.,21.11.13 11:43
-30,ENERCON E 33,330000,0.0,,0.0,,0.0,,5000.0,,13700.0,,30000.0,,55000.0,,92000.0,,138000.0,,196000.0,,250000.0,,292800.0,,320000.0,,335000.0,,335000.0,,335000.0,,335000.0,,335000.0,,335000.0,,335000.0,,335000.0,,335000.0,,335000.0,,335000.0,,335000.0,,335000.0,0.0,,k.A.,21.11.13 11:43
-31,ENERCON E 40 500,500000,0.0,,0.0,,0.0,,4370.0,,18870.0,,40390.0,,88020.0,,112190.0,,165380.0,,236890.0,,328000.0,,396640.0,,465150.0,,495000.0,,500000.0,,500000.0,,500000.0,,500000.0,,500000.0,,500000.0,,500000.0,,500000.0,,500000.0,,500000.0,,500000.0,,500000.0,0.0,,k.A.,21.11.13 11:43
-32,ENERCON E 40 600,600000,0.0,,0.0,,1700.0,,14740.0,,40800.0,,79640.0,,134800.0,,207100.0,,292600.0,,403400.0,,508100.0,,554600.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,0.0,,k.A.,21.11.13 11:43
-33,ENERCON E 44 600,600000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-34,ENERCON E 48 800,800000,0.0,,0.0,,2000.0,,12000.0,,32000.0,,66000.0,,120000.0,,191000.0,,284000.0,,405000.0,,555000.0,,671000.0,,750000.0,,790000.0,,810000.0,,810000.0,,810000.0,,810000.0,,810000.0,,810000.0,,810000.0,,810000.0,,810000.0,,810000.0,,810000.0,,810000.0,0.0,,k.A.,21.11.13 11:43
-35,ENERCON E 53 800,800000,0.0,,0.0,,2000.0,,14000.0,,38000.0,,77000.0,,141000.0,,228000.0,,336000.0,,480000.0,,645000.0,,744000.0,,780000.0,,810000.0,,810000.0,,810000.0,,810000.0,,810000.0,,810000.0,,810000.0,,810000.0,,810000.0,,810000.0,,810000.0,,810000.0,,810000.0,0.0,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-36,ENERCON E 58 1000,1000000,0.0,,0.0,,0.0,,4700.0,,28600.0,,72300.0,,140200.0,,233800.0,,381300.0,,545800.0,,725300.0,,905000.0,,987900.0,,1000000.0,,1000000.0,,1000000.0,,1000000.0,,1000000.0,,1000000.0,,1000000.0,,1000000.0,,1000000.0,,1000000.0,,1000000.0,,1000000.0,,1000000.0,0.0,,k.A.,21.11.13 11:43
-37,ENERCON E 66 1500,1500000,0.0,,0.0,,1800.0,,30100.0,,89300.0,,173500.0,,296000.0,,445000.0,,652600.0,,877000.0,,1122000.0,,1247400.0,,1337800.0,,1468800.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,0.0,,k.A.,21.11.13 11:43
-38,ENERCON E 66 1800,1800000,0.0,,0.0,,0.0,,7500.0,,48000.0,,104000.0,,200000.0,,340000.0,,515000.0,,748000.0,,1025000.0,,1348000.0,,1670000.0,,1760000.0,,1800000.0,,1800000.0,,1800000.0,,1800000.0,,1800000.0,,1800000.0,,1800000.0,,1800000.0,,1800000.0,,1800000.0,,1800000.0,,1800000.0,0.0,,k.A.,21.11.13 11:43
-39,ENERCON E 66 2000,2000000,0.0,,0.0,,0.0,,0.0,,48000,,105000,,202000,,337000,,521000,,749000,,1021000,,1353000,,1703000,,1852000,,2005000,,2005000,,2005000,,2005000,,2005000,,2005000,,2005000,,2005000,,2005000,,2005000,,2005000,,2005000,,,,
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-41,ENERCON E 70 2300,2300000,0.0,,0.0,,2000.0,,18000.0,,56000.0,,127000.0,,240000.0,,400000.0,,626000.0,,892000.0,,1223000.0,,1590000.0,,1900000.0,,2080000.0,,2230000.0,,2300000.0,,2310000.0,,2310000.0,,2310000.0,,2310000.0,,2310000.0,,2310000.0,,2310000.0,,2310000.0,,2310000.0,,2310000.0,0.0,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-42,ENERCON E 82 2000,2000000,0.0,,0.0,,3000.0,,25000.0,,82000.0,,174000.0,,321000.0,,532000.0,,815000.0,,1180000.0,,1580000.0,,1810000.0,,1980000.0,,2050000.0,,2050000.0,,2050000.0,,2050000.0,,2050000.0,,2050000.0,,2050000.0,,2050000.0,,2050000.0,,2050000.0,,2050000.0,,2050000.0,,2050000.0,0.0,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-43,ENERCON E 82 2300,2300000,0.0,,0.0,,3000.0,,25000.0,,82000.0,,174000.0,,321000.0,,532000.0,,815000.0,,1180000.0,,1580000.0,,1890000.0,,2100000.0,,2250000.0,,2350000.0,,2350000.0,,2350000.0,,2350000.0,,2350000.0,,2350000.0,,2350000.0,,2350000.0,,2350000.0,,2350000.0,,2350000.0,,2350000.0,0.0,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-44,ENERCON E 82 3000,3000000,0.0,,0.0,,3000.0,,25000.0,,82000.0,,174000.0,,321000.0,,525000.0,,800000.0,,1135000.0,,1510000.0,,1880000.0,,2200000.0,,2480000.0,,2770000.0,,2850000.0,,2910000.0,,3000000.0,,3020000.0,,3020000.0,,3020000.0,,3020000.0,,3020000.0,,3020000.0,,3020000.0,,3020000.0,0.0,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-45,ENERCON E 92 2300,2300000,0.0,,0.0,,3600.0,,29900.0,,98200.0,,208300.0,,384300.0,,637000.0,,975800.0,,1403600.0,,1817800.0,,2088699.9999999998,,2237000.0,,2300000.0,,2350000.0,,2350000.0,,2350000.0,,2350000.0,,2350000.0,,2350000.0,,2350000.0,,2350000.0,,2350000.0,,2350000.0,,2350000.0,,2350000.0,0.0,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-46,ENO 82,2000000,0.0,,0.0,,0.0,,5000.0,,50000.0,,139000.0,,280000.0,,477000.0,,766000.0,,1048000.0,,1347000.0,,1645000.0,,1943000.0,,2031000.0,,2050000.0,,2050000.0,,2050000.0,,2050000.0,,2050000.0,,2050000.0,,2050000.0,,2050000.0,,2050000.0,,2050000.0,,2050000.0,,2050000.0,0.0,,k.A.,21.11.13 11:43
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-48,FRISIA F 48,750000,0.0,,0.0,,0.0,,0.0,,13000.0,,45400.0,,90200.0,,150100.0,,231200.0,,335800.0,,454800.0,,585200.0,,712800.0,,750000.0,,750000.0,,750000.0,,750000.0,,750000.0,,750000.0,,750000.0,,750000.0,0.0,0.0,,0.0,,0.0,,0.0,,0.0,,,k.A.,21.11.13 11:43
-49,FRISIA F 56,850000,0.0,,0.0,,0.0,,0.0,,22900.0,,67100.0,,121900.0,,213700.0,,326700.0,,466100.0,,629600.0,,831900.0,,850000.0,,850000.0,,850000.0,,850000.0,,850000.0,,850000.0,,850000.0,,850000.0,,850000.0,0.0,0.0,,0.0,,0.0,,0.0,,0.0,,,k.A.,21.11.13 11:43
-50,FUHRLÄNDER FL 100 2500,2500000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,32689.999999999996,75090.0,159890.0,239400.0,345410.0,472600.0,594520.0,727030.0,933750.0,1140000.0,1390000.0,1650000.0,1930000.0,2180000.0,2330000.0,2470000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-51,FUHRLÄNDER FL 54 1000,1000000,0.0,,0.0,,0.0,,0.0,,25000.0,,66000.0,,111000.0,,201000.0,,320000.0,,446000.0,,576000.0,,678000.0,,810000.0,,895000.0,,938000.0,,1008000.0,,1030000.0,,1030000.0,,1035000.0,,1032000.0,,1009000.0,,1009000.0,,1009000.0,,1009000.0,,1009000.0,,1009000.0,0.0,,http://www.inl.gov/wind/software/,21.11.13 11:43
-52,FUHRLANDER FL 80 2500,2500000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,30000.0,96290.0,149290.0,197000.0,265900.0,350710.0,446110.0,552120.0,658130.0,817140.0,970850.0,1160000.0,1350000.0,1550000.0,1730000.0,1910000.0,2080000.0,2230000.0,2350000.0,2440000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-53,FUHRLANDER FL 90 2500,2500000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,30000.0,96290.0,165190.0,255300.0,361310.0,462010.0,568020.0,727030.0,891340.0,1080000.0,1280000.0,1480000.0,1690000.0,1900000.0,2140000.0,2290000.0,2420000.0,2480000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-54,FUHRLÄNDER MD 70,1500000,0.0,0.0,0.0,0.0,0.0,,0.0,,29000.0,,97700.0,,192900.0,,304400.0,,493000.0,,713000.0,,978000.0,,1257000.0,,1456000.0,,1496000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,0.0,,k.A.,21.11.13 11:43
-55,FUHRLÄNDER MD 77,1500000,0.0,0.0,0.0,0.0,0.0,0.0,10000.0,29800.0,45580.0,90470.0,143290.0,185540.0,241000.0,309700.0,383680.0,484120.0,611020.0,793470.0,965340.0,1050000.0,1150000.0,1320000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,,0.0,,0.0,,0.0,,0.0,,0.0,,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
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-57,GAMESA G 52 850,850000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,27900.0,46550.0,65200.0,94150.0,123100.0,163050.0,203000.0,255000.0,307000.0,371150.0,435300.0,499900.0,564500.0,624550.0,684600.0,732250.0,779900.0,810250.0,840600.0,844300.0,848000.0,848500.0,849000.0,849500.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-58,GAMESA G 58,850000,0.0,,0.0,,0.0,,9700.0,,33500.0,,78500.0,,145300.0,,236600.0,,358800.0,,513100.0,,669700.0,,778300.0,,828100.0,,844400.0,,848700.0,,849700.0,,849900.0,,850000.0,,850000.0,,850000.0,,850000.0,,850000.0,,850000.0,,850000.0,,0.0,,0.0,,,k.A.,21.11.13 11:43
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-61,GAMESA G 90 2000,2000000,0.0,0.0,0.0,0.0,0.0,0.0,21300.0,53100.0,84900.0,141100.0,197300.0,280550.0,363800.0,479350.0,594900.0,747850.0,900800.0,1087600.0,1274400.0,1453700.0,1633000.0,1748000.0,1863000.0,1911700.0,1960400.0,1975400.0,1990400.0,1994150.0,1997900.0,1998750.0,1999600.0,1999750.0,1999900.0,1999950.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,,1906000.0,,1681000.0,,1455000.0,,1230000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
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-73,MITSUBISHI MWT 1000A,1000000,0.0,0.0,0.0,0.0,0.0,0.0,780.0,27170.0,53560.0,90830.0,131210.0,182450.0,246120.0,305120.0,365680.0,444880.0,519409.99999999994,592390.0,676240.0,749220.0,826850.0,887420.0,937110.0,975930.0,989910.0,1000000.0,1000000.0,999220.0,999220.0,999220.0,999220.0,1000000.0,1000000.0,1000000.0,1000000.0,1000000.0,1000000.0,1000000.0,1000000.0,1000000.0,1000000.0,1000000.0,1000000.0,1000000.0,1000000.0,1000000.0,1000000.0,1000000.0,1000000.0,1000000.0,1000000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-74,MITSUBISHI MWT 92 2.4,2400000,0.0,0.0,0.0,0.0,0.0,0.0,780.0,64720.0,105090.0,184890.0,264470.0,344050.0,463060.0,601450.0,739830.0,917650.0,1090000.0,1300000.0,1520000.0,1780000.0,1960000.0,2120000.0,2240000.0,2360000.0,2390000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-75,MITSUBISHI MWT 95 2.4,2400000,0.0,0.0,0.0,0.0,0.0,0.0,780.0,41920.0,81140.0,139780.0,237330.0,334810.0,432280.0,549180.0,685490.0,860720.0,1030000.0,1210000.0,1400000.0,1600000.0,1790000.0,1960000.0,2120000.0,2290000.0,2370000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-76,NEG MICON NM 44 750,750000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9110.0,16890.0,32340.000000000004,51600.0,74660.0,105380.0,136090.0,170600.0,212770.0,254920.0,297080.0,339250.0,381390.0,423560.0,469540.0,511690.0,553850.0,592180.0,634350.0,676510.0,707210.0,730290.0,738100.0,749710.0,749890.0,750070.0,750230.0,746580.0,739140.0,731680.0,724210.0,716760.0,705480.0,705650.0,705810.0,706000.0,706180.0,710160.0,710340.0,710520.0,710680.0,710850.0,711030.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-77,NEG MICON NM 48 750,750000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9110.0,19690.0,34170.0,54410.0,80390.0,115000.0,155340.0,207190.0,250410.0,308000.0,359830.0,414560.0,460660.0,518250.0,552850.0,593200.0,627800.0,656660.0,685520.0,705750.0,723110.0,737600.0,749210.0,752200.0,755200.0,755320.0,755440.0,752690.0,749930.0,741430.0,735800.0,727300.0,715930.0,707430.0,698930.0,696170.0,693420.0,693540.0,693650.0,693780.0,696770.0,699760.0,696950.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-78,NEG MICON NM 60 1000,1000000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-79,NEG MICON NM 64C 1500,1500000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-80,NEG MICON NM 72C 1500,1500000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,74000.0,138000.0,202000.0,279000.0,356000.0,466000.0,576000.0,692000.0,808000.0,933000.0,1058000.0,1171500.0,1285000.0,1355500.0,1426000.0,1438500.0,1451000.0,1467000.0,1483000.0,1491500.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-81,NEG MICON NM 82 1500,1500000,0.0,,0.0,,0.0,,0.0,,69000.0,,173000.0,,307000.0,,510000.0,,752000.0,,1001000.0,,1262000.0,,1433000.0,,1490000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,0.0,,0.0,,0.0,,0.0,,0.0,,0.0,,0.0,,,k.A.,21.11.13 11:43
-82,NEG MICON NM 82 1650,1650000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,28000.0,86000.0,144000.0,226500.0,309000.0,410000.0,511000.0,634500.0,758000.0,887500.0,1017000.0,1137500.0,1258000.0,1381000.0,1504000.0,1570500.0,1637000.0,1642500.0,1648000.0,1649000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,,0.0,,0.0,,0.0,,0.0,,0.0,,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-83,NORDEX N 100 2500,2500000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,25000.0,50000.0,135500.0,221000.0,326000.0,431000.0,575500.0,720000.0,911000.0,1102000.0,1338500.0,1575000.0,1797000.0,2019000.0,2161500.0,2304000.0,2381000.0,2458000.0,2479000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-84,NORDEX N 100 3300,3300000,0.0,,0.0,,0.0,,0.0,13000.0,69000.0,136000.0,219000.0,317000.0,434000.0,569000.0,725000.0,903000.0,1106000.0,1334000.0,1583000.0,1852000.0,2136000.0,2430000.0,2698000.0,2907000.0,3067000.0,3182000.0,3256000.0,3294000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,0.0,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-85,NORDEX N 117 2400,2400000,0.0,,0.0,,0.0,,25000.0,82000.0,154000.0,244000.0,354000.0,486000.0,643000.0,827000.0,1038000.0,1272000.0,1525000.0,1794000.0,2037000.0,2211000.0,2326000.0,2386000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,2400000.0,,0.0,,0.0,,0.0,,0.0,,0.0,,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-86,NORDEX N 117 3000,3000000,0.0,,0.0,,0.0,,16000.0,56000.0,129000.0,221000.0,333000.0,467000.0,624000.0,807000.0,1020000.0,1263000.0,1534000.0,1825000.0,2131000.0,2420000.0,2643000.0,2807000.0,2916000.0,2978000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,0.0,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-87,NORDEX N 27 150,150000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-88,NORDEX N 27 250,250000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-89,NORDEX N 29,250000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-90,NORDEX N 43 600,600000,0.0,0.0,0.0,0.0,0.0,0.0,1560.0,9360.0,17160.0,30905.0,44650.0,58335.0,72020.0,97865.0,123710.0,159980.0,196250.0,236525.0,276800.0,320215.0,363630.0,403955.0,444280.0,488930.0,533580.0,558665.0,583750.0,601215.0,618680.0,619085.0,619490.0,618305.0,617120.0,599100.0,599100.0,599100.0,599100.0,599100.0,599100.0,599100.0,599100.0,599100.0,599100.0,599100.0,599100.0,599100.0,599100.0,599100.0,599100.0,599100.0,599100.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-91,NORDEX N 52,800000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-92,NORDEX N 54,1000000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-93,NORDEX N 60 1300,1300000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14500.0,29000.0,51000.0,73000.0,102000.0,131000.0,186000.0,241000.0,308500.0,376000.0,456000.0,536000.0,620000.0,704000.0,787500.0,871000.0,943500.0,1016000.0,1070000.0,1124000.0,1185500.0,1247000.0,1274000.0,1301000.0,1322500.0,1344000.0,1354000.0,1364000.0,1343000.0,1322000.0,1320500.0,1319000.0,1316500.0,1314000.0,1313000.0,1312000.0,1309500.0,1307000.0,1303000.0,1299000.0,1295500.0,1292000.0,1292000.0,1292000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-94,NORDEX N 62,1300000,0.0,,0.0,,0.0,,0.0,,20000.0,,81000.0,,159000.0,,225000.0,,385000.0,,571000.0,,760000.0,,925000.0,,1056000.0,,1168000.0,,1250000.0,,1294000.0,,1300000.0,,1287000.0,,1262000.0,,1232000.0,,1203000.0,,1179000.0,,1158000.0,,1146000.0,,1140000.0,,1138000.0,0.0,,http://www.inl.gov/wind/software/,21.11.13 11:43
-95,NORDEX N 80 2500,2500000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7500.0,15000.0,68000.0,121000.0,186000.0,251000.0,342000.0,433000.0,550000.0,667000.0,820500.0,974000.0,1146500.0,1319000.0,1497000.0,1675000.0,1839500.0,2004000.0,2142500.0,2281000.0,2372000.0,2463000.0,2481500.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-96,NORDEX N 90 2300,2300000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,17500.0,35000.0,105000.0,175000.0,263500.0,352000.0,466000.0,580000.0,725000.0,870000.0,1053500.0,1237000.0,1430000.0,1623000.0,1817500.0,2012000.0,2121000.0,2230000.0,2265000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-97,NORDEX N 90 2500 HS,2500000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5000.0,40000.0,103000.0,170000.0,249000.0,341000.0,448000.0,572000.0,716000.0,880000.0,1062000.0,1262000.0,1475000.0,1702000.0,1928000.0,2116000.0,2262000.0,2370000.0,2443000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-98,NORDEX N 90 2500 LS,2500000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5000.0,35000.0,98000.0,175000.0,260000.0,352000.0,462000.0,580000.0,717000.0,870000.0,1045000.0,1237000.0,1430000.0,1623000.0,1835000.0,2043000.0,2200000.0,2345000.0,2430000.0,2475000.0,2490000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-99,NORDEX S 70 1500,1500000,0.0,,0.0,,0.0,,0.0,,24000.0,,86000.0,,188000.0,,326000.0,,526000.0,,728000.0,,1006000.0,,1271000.0,,1412000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,0.0,,http://www.inl.gov/wind/software/,21.11.13 11:43
-100,NORDEX S 77 1500,1500000,0.0,,0.0,,0.0,,0.0,,25000.0,,87000.0,,214000.0,,377000.0,,589000.0,,855000.0,,1162000.0,,1453000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,0.0,,http://www.inl.gov/wind/software/,21.11.13 11:43
-101,NORDTANK NTK 500,500000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-102,NORDTANK NTK 600,600000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-103,REPOWER 3.0 M122,3000000,0.0,,0.0,,0.0,,27000.0,,157000.0,,363000.0,,675000.0,,1121000.0,,1650000.0,,2231000.0,,2749000.0,,2948000.0,,2970000.0,,2970000.0,,2970000.0,,2970000.0,,2970000.0,,2970000.0,,2970000.0,,2970000.0,,2970000.0,,2970000.0,,2970000.0,,0.0,,0.0,,0.0,,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-104,REPOWER 3.2 M114,3200000,0.0,,0.0,,0.0,,27000.0,,151000.0,,327000.0,,598000.0,,978000.0,,1471000.0,,2031000.0,,2594000.0,,3033000.0,,3200000.0,,3200000.0,,3200000.0,,3200000.0,,3200000.0,,3200000.0,,3200000.0,,3200000.0,,3200000.0,,3200000.0,,3200000.0,,0.0,,0.0,,0.0,,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-105,REPOWER 3.4 M104,3400000,0.0,0.0,0.0,0.0,0.0,,0.0,37000.0,94000.0,,253000.0,,479000.0,,791000.0,,1219000.0,,1745000.0,,2309000.0,,2860000.0,,3295000.0,,3370000.0,,3400000.0,,3400000.0,,3400000.0,,3400000.0,,3400000.0,,3400000.0,,3400000.0,,3400000.0,,3400000.0,,3400000.0,,3400000.0,,3400000.0,0.0,,k.A.,21.11.13 11:43
-106,REPOWER 5M,5000000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,50000.0,137500.0,225000.0,377500.0,530000.0,735000.0,940000.0,1205000.0,1470000.0,1805000.0,2140000.0,2540000.0,2940000.0,3387500.0,3835000.0,4192500.0,4550000.0,4725000.0,4900000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,5000000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-107,REPOWER J 43 500,500000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-108,REPOWER J 48 600,600000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-109,REPOWER J 57 1000,1050000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-110,REPOWER MD 70 1500,1500000,0.0,,0.0,,0.0,,0.0,,24000.0,,86000.0,,188000.0,,326000.0,,525000.0,,728000.0,,1006000.0,,1271000.0,,1412000.0,,1497000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,0.0,,http://www.inl.gov/wind/software/,21.11.13 11:43
-111,REPOWER MD 77,1500000,0.0,,0.0,,0.0,,2000.0,,44000.0,,129000.0,,241000.0,,396000.0,,594000.0,,846000.0,,1100000.0,,1318000.0,,1467000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,0.0,,0.0,,0.0,,0.0,,0.0,,,http://www.inl.gov/wind/software/,21.11.13 11:43
-112,REPOWER MM 100 2000,2000000,0.0,,0.0,,0.0,,20000.0,,102000.0,,239000.0,,452000.0,,746000.0,,1126000.0,,1559000.0,,1901000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,0.0,0.0,,0.0,,0.0,,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-113,REPOWER MM 70,2000000,0.0,,0.0,,0.0,,0.0,,18000.0,,94000.0,,197000.0,,333000.0,,512000.0,,735000.0,,1009000.0,,1305000.0,,1597000.0,,1865000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,,2000000.0,0.0,,http://www.inl.gov/wind/software/,21.11.13 11:43
-114,REPOWER MM 82 2050,2050000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,13480.0,28260.0,63650.0,109080.0,164560.0,225070.0,305680.0,386280.0,466870.0,577630.0,683340.0,804130.0,945030.0,1080000.0,1220000.0,1380000.0,1510000.0,1640000.0,1770000.0,1870000.0,1940000.0,2010000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-115,REPOWER MM 92 2050,2050000,0.0,0.0,0.0,0.0,0.0,0.0,4493.33,13480.0,55190.0,96400.0,152760.0,224110.0,305580.0,386990.0,508660.0,610150.0,741850.0,883540.0,1050000.0,1210000.0,1380000.0,1590000.0,1770000.0,1930000.0,2030000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,2050000.0,,0.0,,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-116,SIEMENS SWT 2.3 93,2300000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,38515.0,95400.0,123380.0,173080.0,258000.0,355890.0,475850.0,608780.0,746260.0,883560.0,1040000.0,1200000.0,1350000.0,1520000.0,1690000.0,1830000.0,1950000.0,2090000.0,2190000.0,2260000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,2300000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-117,SIEMENS SWT 3.6 120,3600000,0.0,,0.0,,0.0,,0.0,,161000.0,,351000.0,,635000.0,,1026000.0,,1544000.0,,2204000.0,,2910000.0,,3399000.0,,3567000.0,,3596000.0,,3600000.0,,3600000.0,,3600000.0,,3600000.0,,3600000.0,,3600000.0,,3600000.0,,3600000.0,,3600000.0,,3600000.0,,3600000.0,,3600000.0,0.0,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-118,SÜDWIND N 3127,270000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-119,SÜDWIND S 31,250000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-120,SÜDWIND S 46,600000,0.0,,0.0,,0.0,,4000.0,,17000.0,,44000.0,,87000.0,,145000.0,,227000.0,,327000.0,,454000.0,,590000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,,600000.0,0.0,,k.A.,21.11.13 11:43
-121,SUZLON S 52 600,600000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1000.0,17500.0,34000.0,65000.0,96000.0,134500.0,173000.0,221000.0,269000.0,320500.0,372000.0,421500.0,471000.0,508500.0,546000.0,565000.0,584000.0,592000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-122,SUZLON S 64 1250,1250000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8750.0,35000.0,62000.0,89000.0,118500.0,148000.0,211500.0,275000.0,360500.0,446000.0,533500.0,621000.0,716000.0,811000.0,900500.0,990000.0,1058500.0,1127000.0,1162500.0,1198000.0,1224000.0,1250000.0,1250000.0,1250000.0,1250000.0,1250000.0,1250000.0,1250000.0,1250000.0,1250000.0,1250000.0,1250000.0,1250000.0,1250000.0,1250000.0,1250000.0,1250000.0,1250000.0,1250000.0,1250000.0,1250000.0,1250000.0,1250000.0,1250000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-123,SUZLON S 64 950,950000,0.0,0.0,0.0,0.0,0.0,0.0,16000.0,26500.0,37000.0,68500.0,100000.0,140500.0,181000.0,234000.0,287000.0,369500.0,452000.0,548500.0,645000.0,753000.0,861000.0,905500.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,950000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-124,SUZLON S 82 1500,1500000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1000.0,50000.0,99000.0,181000.0,263000.0,364500.0,466000.0,587500.0,709000.0,841500.0,974000.0,1096500.0,1219000.0,1300500.0,1382000.0,1410000.0,1438000.0,1455500.0,1473000.0,1486500.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,,0.0,,0.0,,0.0,,0.0,,0.0,,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-125,SUZLON S 88,2100000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14000.0,76000.0,138000.0,225000.0,312000.0,429000.0,546000.0,693000.0,840000.0,1010000.0,1180000.0,1357500.0,1535000.0,1695500.0,1856000.0,1946500.0,2037000.0,2062500.0,2088000.0,2094000.0,2100000.0,2100000.0,2100000.0,2100000.0,2100000.0,2100000.0,2100000.0,2100000.0,2100000.0,2100000.0,2100000.0,2100000.0,2100000.0,2100000.0,2100000.0,2100000.0,2100000.0,2100000.0,2100000.0,2100000.0,2100000.0,2100000.0,2100000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-126,TACKE T 600 48,600000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,22500.0,35250.0,48000.0,67100.0,86200.0,112150.0,138100.0,173950.0,209800.0,247900.0,286000.0,326700.0,367400.0,408500.0,449600.0,487350.0,525100.0,544700.0,564300.0,581900.0,599500.0,604400.0,609300.0,612750.0,616200.0,613250.0,610300.0,605400.0,600500.0,595100.0,589700.0,595600.0,601500.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-127,TACKE TW 600,600000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-128,TACKE TW 600E,600000,0.0,0.0,0.0,0.0,0.0,0.0,12800.0,16460.0,23780.0,38410.0,56710.0,75000.0,100600.0,137200.0,173780.0,210370.0,246950.0,312800.0,367680.0,411580.0,462800.0,499390.0,532320.0,557930.0,579880.0,600000.0,605490.0,609150.0,612800.0,612800.0,616460.0,616460.0,616460.0,616460.0,620120.0,623780.0,623780.0,623780.0,623780.0,623780.0,623780.0,623780.0,627440.0,627440.0,627440.0,627440.0,627440.0,627440.0,627440.0,627440.0,627440.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-129,VENSYS 100,2500000,0.0,,0.0,,0.0,,47200.0,81900.0,125400.0,180200.0,250100.0,334500.0,437400.0,557200.0,696400.0,853700.0,1034800.0,1236400.0,1449300.0,1675800.0,1904100.0,2116800.0,2285500.0,2389000.0,2472100.0,2492000.0,2499400.0,,2500000.0,,2500000.0,,2500000.0,,2500000.0,,2500000.0,,2500000.0,,2500000.0,,2500000.0,,2500000.0,,2500000.0,,2500000.0,,2500000.0,0.0,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-130,VENSYS 109,2500000,0.0,,0.0,,0.0,,54700.0,97400.0,151200.0,216500.0,298100.0,398600.0,518200.00000000006,659300.0,821700.0,1009300.0,1222800.0,1461500.0,1720100.0,1966100.0,2193000.0,2357700.0,2448700.0,2485500.0,2494200.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,0.0,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-131,VENSYS 112,2500000,0.0,,0.0,,0.0,,47400.0,93600.0,154200.0,228000.0,315900.0,421200.0,548500.0,697300.0,871100.0,1068000.0,1290100.0,1538400.0,1808800.0,2071699.9999999998,2269500.0,2412600.0,2465100.0,2497900.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,2500000.0,0.0,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-132,VENSYS 77,1500000,0.0,,0.0,,0.0,,18300.0,41400.0,70800.0,105600.0,147700.0,199300.0,262700.0,334500.0,420300.0,519600.0,633700.0,758400.0,892500.0,1032300.0,1161500.0,1287600.0,1372700.0,1440000.0,1468300.0,1491800.0,1495600.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,0.0,,k.A.,21.11.13 11:43
-133,VENSYS 82,1500000,0.0,,0.0,,0.0,,25900.0,52400.0,85800.0,125700.0,175100.0,236200.0,311000.0,396100.0,496800.0,614400.0,746800.0,889700.0,1040599.9999999999,1185000.0,1309400.0,1402700.0,1461600.0,1483900.0,1491200.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,1500000.0,,0.0,,0.0,,0.0,,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
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-135,VESTAS V 100 1800,1800000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,119310.0,181120.0,266950.0,359660.0,469530.0,600000.0,747640.0,919310.0,1110000.0,1290000.0,1460000.0,1620000.0,1730000.0,1770000.0,1790000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-136,VESTAS V 112 3000,3000000,0.0,0.0,0.0,0.0,0.0,0.0,72120.0,106730.0,170190.0,245190.0,337500.0,441350.0,568270.0,729810.0,925960.0,1170000.0,1420000.0,1680000.0,1960000.0,2270000.0,2560000.0,2810000.0,2930000.0,2980000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-137,VESTAS V 117 3300,3300000,0.0,,0.0,,0.0,,29000.0,81000.0,146000.0,226000.0,327000.0,452000.0,600000.0,775000.0,981000.0,1216000.0,1483000.0,1780000.0,2103000.0,2437000.0,2753000.0,2999000.0,3159000.0,3241000.0,3276000.0,3290000.0,3297000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,0.0,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-138,VESTAS V 126 3300,3300000,0.0,,0.0,,0.0,,36000.0,99000.0,177000.0,274000.0,393000.0,537000.0,712000.0,919000.0,1161000.0,1436000.0,1747000.0,2082000.0,2421000.0,2741000.0,3011000.0,3185000.0,3267000.0,3293000.0,3298000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,3300000.0,0.0,,0.0,,0.0,,,http://www.windenergie-im-binnenland.de,21.11.13 11:43
-139,VESTAS V 29,225000,0.0,,0.0,,0.0,,0.0,,7100.0,,20500.0,,38300.0,,61900.0,,92200.0,,128000.0,,165000.0,,196000.0,,216000.0,,223000.0,,225000.0,,225000.0,,225000.0,,225000.0,,225000.0,,225000.0,,225000.0,,225000.0,,225000.0,,225000.0,,225000.0,,225000.0,0.0,,http://www.inl.gov/wind/software/,21.11.13 11:43
-140,VESTAS V 39 500,500000,0.0,,0.0,,0.0,,0.0,,0.0,,18000.0,,60000.0,,105000.0,,163000.0,,232000.0,,305000.0,,375000.0,,440000.0,,478000.0,,493000.0,,498500.0,,499700.0,,500000.0,,500000.0,,500000.0,,500000.0,,500000.0,,500000.0,,500000.0,,500000.0,,500000.0,0.0,,http://www.inl.gov/wind/software/,21.11.13 11:43
-141,VESTAS V 42 600,600000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21500.0,43350.0,65200.0,92600.0,120000.0,154000.0,188000.0,228000.0,268000.0,312000.0,356000.0,398000.0,440000.0,475000.0,510000.0,533000.0,556000.0,569000.0,582000.0,588000.0,594000.0,596000.0,598000.0,599000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-142,VESTAS V 44 600,600000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,30400.0,53850.0,77300.0,106150.0,135000.0,170500.0,206000.0,246500.0,287000.0,329000.0,371000.0,410500.0,450000.0,482000.0,514000.0,536000.0,558000.0,570000.0,582000.0,588000.0,594000.0,596000.0,598000.0,599000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,600000.0,,0.0,,0.0,,0.0,,0.0,,0.0,,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-143,VESTAS V 47 660,660000,0.0,0.0,0.0,0.0,0.0,,0.0,,5300.0,,44900.0,,95400.0,,161000.0,,242000.0,,334000.0,,426000.0,,511000.0,,577000.0,,620000.0,,644000.0,,654000.0,,658000.0,,660000.0,,660000.0,,660000.0,,660000.0,,660000.0,,660000.0,,660000.0,,660000.0,,660000.0,0.0,,k.A.,21.11.13 11:43
-144,VESTAS V 52 850,850000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,9110.0,22480.0,37990.0,59600.0,81180.0,105830.0,130430.0,170190.0,206960.0,252770.0,301660.0,344450.0,396330.0,454310.0,515280.0,567190.0,609980.0,652770.0,698630.0,741410.0,778180.0,802790.0,818300.0,836880.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,850000.0,0.0,0.0,,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-145,VESTAS V 66 1650,1650000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7500.0,37150.0,66800.0,105400.0,144000.0,196700.0,249400.0,318800.0,388200.0,474000.0,559800.0,653800.0,747800.0,840800.0,933800.0,1020700.0,1107600.0,1188100.0,1268600.0,1341300.0,1414000.0,1471800.0,1529600.0,1566000.0,1602400.0,1619200.0,1636000.0,1641000.0,1646000.0,1648000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-146,VESTAS V 66 1750,1750000,0.0,,0.0,,0.0,,0.0,,33300.0,,93900.0,,178000.0,,294000.0,,452000.0,,655000.0,,900000.0,,1167000.0,,1418000.0,,1603000.0,,1702000.0,,1739000.0,,1748000.0,,1750000.0,,1750000.0,,1750000.0,,1750000.0,,1750000.0,,1750000.0,,1750000.0,,1750000.0,,1750000.0,0.0,,k.A.,21.11.13 11:43
-147,VESTAS V 80 1800,1800000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3000.0,51000.0,99000.0,179500.0,260000.0,362500.0,465000.0,600000.0,735000.0,875000.0,1015000.0,1180000.0,1345000.0,1492000.0,1639000.0,1707000.0,1775000.0,1786000.0,1797000.0,1799500.0,1802000.0,1802000.0,1802000.0,1802000.0,1802000.0,1802000.0,1802000.0,1802000.0,1802000.0,1802000.0,1802000.0,1802000.0,1802000.0,1802000.0,1802000.0,1802000.0,1802000.0,1802000.0,1802000.0,1801000.0,1800000.0,1800000.0,1800000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-148,VESTAS V 80 2000,2000000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,58850.0,72480.0,106710.0,154670.0,223260.0,309030.0,411980.0,525240.0,645370.0,768940.0,899380.0,1030000.0,1170000.0,1300000.0,1420000.0,1550000.0,1660000.0,1750000.0,1830000.0,1890000.0,1930000.0,1950000.0,1970000.0,1985000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-149,VESTAS V 82 1500,1500000,0.0,,0.0,,0.0,,0.0,,69000.0,,173000.0,,307000.0,,510000.0,,752000.0,,1001000.0,,1262000.0,,1433000.0,,1490000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,1500000.0,,0.0,,0.0,,0.0,,0.0,,0.0,,0.0,,0.0,,,k.A.,21.11.13 11:43
-150,VESTAS V 82 1650,1650000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,18920.0,73810.0,147790.0,237300.0,338470.0,451290.0,567990.0,680810.0,797510.0,921970.0,1040000.0,1160000.0,1260000.0,1360000.0,1440000.0,1500000.0,1550000.0,1580000.0,1610000.0,1630000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,1650000.0,,0.0,,0.0,,0.0,,0.0,,0.0,,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-151,VESTAS V 90 1800,1800000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,18560.0,52210.0,96880.0,157990.0,241120.0,335200.0,423780.0,556370.0,683410.0,832430.0,992490.0,1140000.0,1310000.0,1480000.0,1640000.0,1750000.0,1790000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,1800000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-152,VESTAS V 90 2000,2000000,0.0,0.0,0.0,0.0,0.0,4595.0,13785.0,29560.0,57700.0,102370.0,157990.0,241120.0,329710.0,429280.0,556370.0,677920.0,826940.0,997990.0,1190000.0,1350000.0,1560000.0,1730000.0,1860000.0,1950000.0,1970000.0,1990000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,2000000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-153,VESTAS V 90 3000,3000000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,26442.5,88141.7,129410.0,182530.0,247120.0,317480.0,422540.0,515980.0,644030.0,795170.0,992550.0,1200000.0,1380000.0,1560000.0,1750000.0,1950000.0,2140000.0,2340000.0,2540000.0,2730000.0,2850000.0,2910000.0,2970000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,3000000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-154,WINDWORLD W 42 500,500000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-155,WINDWORLD W 52 750,750000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-156,WTN WTN 200,200000,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,k.A.,21.11.13 11:43
-157,ZOND Z 40 550,550000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1670.0,10580.0,26720.0,37430.0,55380.0,82350.0,111140.0,136310.0,165100.0,192080.0,224480.0,258690.0,291090.0,341560.0,384810.0,404560.0,442380.0,478390.0,503560.0,537770.0,550000.0,550000.0,550000.0,550000.0,550000.0,550000.0,550000.0,550000.0,550000.0,550000.0,550000.0,550000.0,550000.0,550000.0,550000.0,550000.0,550000.0,550000.0,550000.0,550000.0,550000.0,550000.0,550000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-158,ZOND Z 50 750,750000,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8290.0,28000.0,53390.0,78830.0,116110.0,147340.0,190160.0,244810.0,293410.0,353580.0,419810.0,480010.0,540000.0,600630.0,666850.0,721250.0,746690.0,750000.0,750000.0,750000.0,750000.0,750000.0,750000.0,750000.0,750000.0,750000.0,750000.0,750000.0,750000.0,750000.0,750000.0,750000.0,750000.0,750000.0,750000.0,750000.0,750000.0,750000.0,750000.0,750000.0,750000.0,750000.0,750000.0,0.0,,https://sam.nrel.gov/content/component-databases,21.11.13 11:43
-159,SENVION 3.2M114,3200000,0.0,,0.0,,0.0,,23000.0,,139000.0,,330000.0,,604000.0,,990000.0,,1502000.0,,2102000.0,,2697000.0,,3098000.0,,3200000.0,,3200000.0,,3200000.0,,3200000.0,,3200000.0,,3200000.0,,3200000.0,,3200000.0,,3200000.0,,3200000.0,,3200000.0,,0.0,,0.0,,0.0,0.0,,https://www.wind-turbine-models.com/turbines/882-senvion-3.2m114#powercurve,06.12.2018 12:59
diff --git a/windpowerlib/modelchain.py b/windpowerlib/modelchain.py
index 2efca922..5beec1e1 100644
--- a/windpowerlib/modelchain.py
+++ b/windpowerlib/modelchain.py
@@ -91,8 +91,9 @@ class ModelChain(object):
     >>> enerconE126 = {
     ...    'hub_height': 135,
     ...    'rotor_diameter': 127,
-    ...    'name': 'ENERCON E 126 7500',
-    ...    'fetch_curve': 'power_curve'}
+    ...    'name': 'E-126/4200',
+    ...    'fetch_curve': 'power_curve',
+    ...     'data_source': 'oedb'}
     >>> e126 = wind_turbine.WindTurbine(**enerconE126)
     >>> modelchain_data = {'density_model': 'ideal_gas'}
     >>> e126_mc = modelchain.ModelChain(e126, **modelchain_data)
@@ -315,9 +316,9 @@ def wind_speed_hub(self, weather_df):
                 "or 'log_interpolation_extrapolation'.")
         return wind_speed_hub
 
-    def turbine_power_output(self, wind_speed_hub, density_hub):
+    def calculate_power_output(self, wind_speed_hub, density_hub):
         r"""
-        Calculates the power output of the wind turbine.
+        Calculates the power output of the wind turbine. # todo power plant output????
 
         The method specified by the parameter `power_output_model` is used.
 
@@ -418,6 +419,6 @@ def run_model(self, weather_df):
         density_hub = (None if (self.power_output_model == 'power_curve' and
                                 self.density_correction is False)
                        else self.density_hub(weather_df))
-        self.power_output = self.turbine_power_output(wind_speed_hub,
-                                                      density_hub)
+        self.power_output = self.calculate_power_output(wind_speed_hub,
+                                                        density_hub)
         return self
diff --git a/windpowerlib/turbine_cluster_modelchain.py b/windpowerlib/turbine_cluster_modelchain.py
index 4741548d..25544236 100644
--- a/windpowerlib/turbine_cluster_modelchain.py
+++ b/windpowerlib/turbine_cluster_modelchain.py
@@ -273,6 +273,6 @@ def run_model(self, weather_df):
             wind_speed_hub = wake_losses.reduce_wind_speed(
                 wind_speed_hub,
                 wind_efficiency_curve_name=self.wake_losses_model)
-        self.power_output = self.turbine_power_output(wind_speed_hub,
-                                                      density_hub)
+        self.power_output = self.calculate_power_output(wind_speed_hub,
+                                                        density_hub)
         return self
diff --git a/windpowerlib/wind_farm.py b/windpowerlib/wind_farm.py
index e370fb9e..f6ca37c9 100644
--- a/windpowerlib/wind_farm.py
+++ b/windpowerlib/wind_farm.py
@@ -68,8 +68,9 @@ class WindFarm(object):
     >>> enerconE126 = {
     ...    'hub_height': 135,
     ...    'rotor_diameter': 127,
-    ...    'name': 'ENERCON E 126 7500',
-    ...    'fetch_curve': 'power_curve'}
+    ...    'name': 'E-126/4200',
+    ...    'fetch_curve': 'power_curve',
+    ...    'data_source': 'oedb'}
     >>> e126 = wind_turbine.WindTurbine(**enerconE126)
     >>> example_farm_data = {
     ...    'name': 'example_farm',
@@ -78,7 +79,7 @@ class WindFarm(object):
     >>> example_farm = wind_farm.WindFarm(**example_farm_data)
     >>> example_farm.installed_power = example_farm.get_installed_power()
     >>> print(example_farm.installed_power)
-    45000000
+    25200000.0
 
     """
     def __init__(self, name, wind_turbine_fleet, coordinates=None,
@@ -260,14 +261,16 @@ def assign_power_curve(self, wake_losses_model='power_efficiency_curve',
                 # can occure problems during the aggregation
                 if power_curve.iloc[0]['wind_speed'] != 0.0:
                     power_curve = pd.concat(
-                        [power_curve, pd.DataFrame(data={
-                            'power': [0.0], 'wind_speed': [0.0]})])
+                        [pd.DataFrame(data={
+                            'power': [0.0], 'wind_speed': [0.0]}),
+                         power_curve], sort=False)
                 if power_curve.iloc[-1]['power'] != 0.0:
                     power_curve = pd.concat(
                         [power_curve, pd.DataFrame(data={
-                            'power': [0.0],
-                            'wind_speed': [power_curve['wind_speed'].loc[
-                                               power_curve.index[-1]] + 0.5]})])
+                            'power': [0.0], 'wind_speed': [
+                                power_curve['wind_speed'].loc[
+                                    power_curve.index[-1]] + 0.5]})],
+                        sort=False)
             # Add power curves of all turbine types to data frame
             # (multiplied by turbine amount)
             df = pd.concat(
diff --git a/windpowerlib/wind_turbine.py b/windpowerlib/wind_turbine.py
index f41b20d0..b79e9e78 100644
--- a/windpowerlib/wind_turbine.py
+++ b/windpowerlib/wind_turbine.py
@@ -14,6 +14,11 @@
 import os
 import numpy as np
 
+try:
+    import requests as rq
+except ImportError:
+    rq = None
+
 
 class WindTurbine(object):
     r"""
@@ -46,13 +51,19 @@ class WindTurbine(object):
     coordinates : list or None
         List of coordinates [lat, lon] of location for loading data.
         Default: None.
+    data_source : string
+        Specifies whether turbine data (f.e. nominal power, power curve, power
+        coefficient curve) is loaded from the Open Energy Database ('oedb') or
+        from a csv file ('<name of csv file>'). See `example_power_curves.csv'
+        and `example_power_coefficient_curves.csv` in windpowerlib/data for
+        the required form of a csv file. Default: 'oedb'.
 
     Attributes
     ----------
     name : string
         Name of the wind turbine type.
-        Use :py:func:`~.get_turbine_types` to see a list of all wind turbines for which
-        power (coefficient) curve data is provided.
+        Use :py:func:`~.get_turbine_types` to see a list of all wind turbines
+        for which power (coefficient) curve data is provided.
     hub_height : float
         Hub height of the wind turbine in m.
     rotor_diameter : None or float
@@ -67,10 +78,6 @@ class WindTurbine(object):
         corresponding power curve value in W. Default: None.
     nominal_power : None or float
         The nominal output of the wind turbine in W.
-    fetch_curve : string
-        Parameter to specify whether a power or power coefficient curve
-        should be retrieved from the provided turbine data. Valid options are
-        'power_curve' and 'power_coefficient_curve'. Default: None.
     coordinates : list or None
         List of coordinates [lat, lon] of location for loading data.
         Default: None.
@@ -80,9 +87,9 @@ class WindTurbine(object):
     Notes
     ------
     Your wind turbine object should have a power coefficient or power curve.
-    You can set the `fetch_curve` parameter if you don't want to provide one
-    yourself but want to automatically fetch a curve from the data set
-    provided along with the windpowerlib.
+    You can set the `fetch_curve` parameter and the `data_source` parameter if
+    you don't want to provide one yourself but want to automatically fetch a
+    curve from a data set provided in the Open Energy Database (oedb).
 
     Examples
     --------
@@ -90,17 +97,19 @@ class WindTurbine(object):
     >>> enerconE126 = {
     ...    'hub_height': 135,
     ...    'rotor_diameter': 127,
-    ...    'name': 'ENERCON E 126 7500',
-    ...    'fetch_curve': 'power_curve'}
+    ...    'name': 'E-126/4200',
+    ...    'fetch_curve': 'power_curve',
+    ...    'data_source': 'oedb'}
     >>> e126 = wind_turbine.WindTurbine(**enerconE126)
     >>> print(e126.nominal_power)
-    7500000
+    4200000.0
 
     """
 
     def __init__(self, name, hub_height, rotor_diameter=None,
                  power_coefficient_curve=None, power_curve=None,
-                 nominal_power=None, fetch_curve=None, coordinates=None):
+                 nominal_power=None, fetch_curve=None, coordinates=None,
+                 data_source='oedb'):
 
         self.name = name
         self.hub_height = hub_height
@@ -113,19 +122,33 @@ def __init__(self, name, hub_height, rotor_diameter=None,
         self.power_output = None
 
         if self.power_coefficient_curve is None and self.power_curve is None:
-            self.fetch_turbine_data(fetch_curve)
+            self.fetch_turbine_data(fetch_curve, data_source)
 
-    def fetch_turbine_data(self, fetch_curve):
+    def fetch_turbine_data(self, fetch_curve, data_source):
         r"""
         Fetches data of the requested wind turbine.
 
         Method fetches nominal power as well as power coefficient curve or
-        power curve from a data file provided along with the windpowerlib.
-        You can also use this function to import your own power (coefficient)
-        curves. Therefore the wind speeds in m/s have to be in the first row
-        and the corresponding power coefficient curve values or power
+        power curve from a data set provided in the Open Energy Database
+        (oedb). You can also use this function to import your own power
+        (coefficient) curves. For that the wind speeds in m/s have to be in the
+        first row and the corresponding power coefficient curve values or power
         curve values in W in a row where the first column contains the turbine
-        name (See directory windpowerlib/data as reference).
+        name (see directory windpowerlib/data as reference).
+
+        Parameters
+        ----------
+        fetch_curve : string
+            Parameter to specify whether a power or power coefficient curve
+            should be retrieved from the provided turbine data. Valid options
+            are 'power_curve' and 'power_coefficient_curve'. Default: None.
+        data_source : string
+            Specifies whether turbine data (f.e. nominal power, power curve,
+            power coefficient curve) is loaded from the Open Energy Database
+            ('oedb') or from a csv file ('<name of csv file>'). See
+            `example_power_curves.csv` and
+            `example_power_coefficient_curves.csv` in windpowerlib/data for the
+            required form of a csv file. Default: 'oedb'.
 
         Returns
         -------
@@ -137,19 +160,32 @@ def fetch_turbine_data(self, fetch_curve):
         >>> enerconE126 = {
         ...    'hub_height': 135,
         ...    'rotor_diameter': 127,
-        ...    'name': 'ENERCON E 126 7500',
-        ...    'fetch_curve': 'power_coefficient_curve'}
+        ...    'name': 'E-126/4200',
+        ...    'fetch_curve': 'power_coefficient_curve',
+        ...    'data_source': 'oedb'}
         >>> e126 = wind_turbine.WindTurbine(**enerconE126)
         >>> print(e126.power_coefficient_curve['power coefficient'][5])
-        0.423
+        0.44
         >>> print(e126.nominal_power)
-        7500000
+        4200000.0
+
+        >>> example_turbine = {
+        ...    'hub_height': 100,
+        ...    'rotor_diameter': 70,
+        ...    'name': 'DUMMY 3',
+        ...    'fetch_curve': 'power_curve',
+        ...    'data_source': 'example_power_curves.csv'}
+        >>> e_t_1 = wind_turbine.WindTurbine(**example_turbine)
+        >>> print(e_t_1.power_curve['power'][7])
+        18000.0
+        >>> print(e_t_1.nominal_power)
+        150000
 
         """
 
         def restructure_data():
             r"""
-            Restructures data read from a csv file.
+            Restructures data fetched from oedb or read from a csv file.
 
             Method creates a two-dimensional DataFrame containing the power
             coefficient curve or power curve of the requested wind turbine.
@@ -164,38 +200,52 @@ def restructure_data():
                 the corresponding wind speeds in m/s.
 
             """
-            df = read_turbine_data(filename=filename)
-            wpp_df = df[df.turbine_id == self.name]
-            # if turbine not in data file
-            if wpp_df.shape[0] == 0:
-                pd.set_option('display.max_rows', len(df))
-                logging.info('Possible types: \n{0}'.format(df.turbine_id))
-                pd.reset_option('display.max_rows')
-                sys.exit('Cannot find the wind converter type: {0}'.format(
-                    self.name))
-            # if turbine in data file write power (coefficient) curve values
-            # to 'data' array
-            ncols = ['turbine_id', 'p_nom', 'source', 'modificationtimestamp']
-            data = np.array([0, 0])
-            for col in wpp_df.keys():
-                if col not in ncols:
-                    if wpp_df[col].iloc[0] is not None and not np.isnan(
-                            float(wpp_df[col].iloc[0])):
-                        data = np.vstack((data, np.array(
-                            [float(col), float(wpp_df[col])])))
-            data = np.delete(data, 0, 0)
+
+            if data_source == 'oedb':
+                df = load_turbine_data_from_oedb()
+                df.set_index('turbine_type', inplace=True)
+                # Set `curve` depending on `fetch_curve` to match names in oedb
+                curve = ('cp_curve' if fetch_curve == 'power_coefficient_curve'
+                         else fetch_curve)
+                data = df.loc[self.name][curve]
+                nominal_power = df.loc[self.name][
+                                    'installed_capacity_kw'] * 1000
+            else:
+                df = read_turbine_data(filename=data_source)
+                wpp_df = df[df.turbine_id == self.name]
+                # if turbine not in data file
+                if wpp_df.shape[0] == 0:
+                    pd.set_option('display.max_rows', len(df))
+                    logging.info('Possible types: \n{0}'.format(df.turbine_id))
+                    pd.reset_option('display.max_rows')
+                    sys.exit('Cannot find the wind converter type: {0}'.format(
+                        self.name))
+                # if turbine in data file write power (coefficient) curve
+                # values to 'data' array
+                ncols = ['turbine_id', 'p_nom', 'source',
+                         'modificationtimestamp']
+                data = np.array([0, 0])
+                for col in wpp_df.keys():
+                    if col not in ncols:
+                        if wpp_df[col].iloc[0] is not None and not np.isnan(
+                                float(wpp_df[col].iloc[0])):
+                            data = np.vstack((data, np.array(
+                                [float(col), float(wpp_df[col])])))
+                data = np.delete(data, 0, 0)
+                nominal_power = wpp_df['p_nom'].iloc[0]
             if fetch_curve == 'power_curve':
                 df = pd.DataFrame(data, columns=['wind_speed', 'power'])
+                if data_source == 'oedb':
+                    # power values in W
+                    df['power'] = df['power'] * 1000
             if fetch_curve == 'power_coefficient_curve':
                 df = pd.DataFrame(data, columns=['wind_speed',
                                                  'power coefficient'])
-            nominal_power = wpp_df['p_nom'].iloc[0]
             return df, nominal_power
+
         if fetch_curve == 'power_curve':
-            filename = 'power_curves.csv'
             self.power_curve, p_nom = restructure_data()
         elif fetch_curve == 'power_coefficient_curve':
-            filename = 'power_coefficient_curves.csv'
             self.power_coefficient_curve, p_nom = restructure_data()
         else:
             raise ValueError("'{0}' is an invalid value. ".format(
@@ -206,74 +256,126 @@ def restructure_data():
         return self
 
 
-def read_turbine_data(**kwargs):
+def read_turbine_data(filename, **kwargs):
     r"""
-    Fetches power (coefficient) curves from a file.
+    Fetches power (coefficient) curves from a  or a file.
+    Turbine data is provided by the Open Energy Database (oedb) or can be
+    provided by the user via a file. In the directory windpowerlib/data example
+    files are provided.
 
-    The data files are provided along with the windpowerlib and are located in
-    the directory windpowerlib/data.
+    Parameters
+    ----------
+    filename : string
+        Specifies the source of the turbine data.
+        Use 'example_power_coefficient_curves.csv' or
+        'example_power_curves.csv' to use the example data.
 
     Other Parameters
     ----------------
     datapath : string, optional
-        Path where the data file is stored. Default: './data'
-    filename : string, optional
-        Name of data file. Provided data files are 'power_curves.csv' and
-        'power_coefficient_curves.csv'. Default: 'power_curves.csv'.
+        Path where the data file is stored if `source` is name of a csv file.
+        Default: './data'
 
     Returns
     -------
     pandas.DataFrame
         Power coefficient curve values (dimensionless) or power curve values
         in kW with corresponding wind speeds in m/s of all available wind
-        turbines with turbine name in column 'turbine_id', turbine nominal
+        turbines with turbine name in column 'turbine_type', turbine nominal
         power in column 'p_nom'.
 
     """
+
     if 'datapath' not in kwargs:
-        kwargs['datapath'] = os.path.join(os.path.dirname(__file__), 'data')
+        kwargs['datapath'] = os.path.join(os.path.dirname(__file__),
+                                          'data')
+    try:
+        df = pd.read_csv(os.path.join(kwargs['datapath'], filename),
+                         index_col=0)
+    except FileNotFoundError:
+        raise FileNotFoundError(
+            "The file '{}' was not found. Check spelling ".format(filename) +
+            "and `datapath` - is '{}' ".format(kwargs['datapath']) +
+            "and can be changed in read_turbine_data()")
+    return df
 
-    if 'filename' not in kwargs:
-        kwargs['filename'] = 'power_curves.csv'
 
-    df = pd.read_csv(os.path.join(kwargs['datapath'], kwargs['filename']),
-                     index_col=0)
-    return df
+def load_turbine_data_from_oedb():
+    r"""
+    Loads turbine data from the Open Energy Database (oedb).
+
+    Returns
+    -------
+    turbine_data : pd.DataFrame
+        Contains turbine data of different turbine types like 'manufacturer',
+        'turbine_type', nominal power ('installed_capacity_kw'), '
 
+    """
 
-def get_turbine_types(print_out=True, **kwargs):
+    if rq:
+        # url of Open Energy Platform that contains the oedb
+        oep_url = 'http://oep.iks.cs.ovgu.de/'
+        # location of data
+        schema = 'model_draft'
+        table = 'openfred_windpower_powercurve'
+        # load data
+        result = rq.get(
+            oep_url + '/api/v0/schema/{}/tables/{}/rows/?'.format(
+                schema, table), )
+        if result.status_code == 200:
+            logging.info("Data base connection successful.")
+        else:
+            raise ConnectionError("Data base connection not successful. " +
+                                  "Error: ".format(result.status_code))
+        # extract data
+        turbine_data = pd.DataFrame(result.json())
+    else:
+        raise ImportError('If you want to load turbine data from the oedb' +
+                          'you have to install the requests package.' +
+                          'see https://pypi.org/project/requests/')
+    return turbine_data
+
+
+def get_turbine_types(print_out=True):
     r"""
     Get the names of all possible wind turbine types for which the power
-    coefficient curve or power curve is provided in the data files in
-    the directory windpowerlib/data.
+    coefficient curve or power curve is provided in the Open Energy Data Base
+    (oedb).
 
     Parameters
     ----------
     print_out : boolean
-        Directly prints the list of types if set to True. Default: True.
-
-    Other Parameters
-    ----------------
-    datapath : string, optional
-        Path where the data file is stored. Default: './data'
-    filename : string, optional
-        Name of data file. Provided data files are 'power_curves.csv' and
-        'power_coefficient_curves.csv'. Default: 'power_curves.csv'.
+        Directly prints a tabular containing the turbine types in column
+        'turbine_type'. Default: True.
 
     Examples
     --------
     >>> from windpowerlib import wind_turbine
-    >>> turbines = wind_turbine.get_turbine_types(print_out=False)
-    >>> print(turbines[turbines["turbine_id"].str.contains("ENERCON")].iloc[0])
-    turbine_id    ENERCON E 101 3000
-    p_nom                    3000000
-    Name: 25, dtype: object
+    >>> df = wind_turbine.get_turbine_types(print_out=False)
+    >>> print(df[df["turbine_type"].str.contains("E-126")].iloc[0])
+    manufacturer          Enercon
+    turbine_type       E-126/4200
+    has_power_curve          True
+    has_cp_curve             True
+    Name: 5, dtype: object
+    >>> print(df[df["manufacturer"].str.contains("Enercon")].iloc[0])
+    manufacturer          Enercon
+    turbine_type       E-101/3050
+    has_power_curve          True
+    has_cp_curve             True
+    Name: 1, dtype: object
 
     """
-    df = read_turbine_data(**kwargs)
 
+    df = load_turbine_data_from_oedb()
+    cp_curves_df = df.iloc[df.loc[df['has_cp_curve'] == True].index][
+        ['manufacturer', 'turbine_type', 'has_cp_curve']]
+    p_curves_df = df.iloc[df.loc[df['has_power_curve'] == True].index][
+        ['manufacturer', 'turbine_type', 'has_power_curve']]
+    curves_df = pd.merge(p_curves_df, cp_curves_df, how='outer',
+                        sort=True).fillna(False)
     if print_out:
-        pd.set_option('display.max_rows', len(df))
-        print(df[['turbine_id', 'p_nom']])
+        pd.set_option('display.max_rows', len(curves_df))
+        print(curves_df)
         pd.reset_option('display.max_rows')
-    return df[['turbine_id', 'p_nom']]
+    return curves_df