Added plot function to plot stacked end-uses

data/residential_model/SANSOM_residential_gas_hourly_shape.csv

@@ -2,3 +2,4 @@
 0.324165148,0.242081993,0.235291247,0.247121803,0.449886154,1.0524659,1.996454351,2.960973492,2.550991999,1.935409798,1.646425392,1.445955613,1.499672576,1.465622279,1.64895767,2.138635797,2.586991342,2.92111233,3.041061223,2.922288612,2.499211394,1.965780442,1.348506963,0.718933986
 0.31130047,0.228049794,0.214258449,0.231862357,0.475644402,1.132311607,2.185429091,3.11326744,2.439908289,1.743192597,1.583259403,1.396992889,1.496813179,1.481353766,1.653440453,2.160328591,2.599554363,2.919720209,3.088836926,2.977139045,2.527249428,1.999918409,1.360436874,0.702586273
 0.356326843,0.277162489,0.287873242,0.285270418,0.385490537,0.852851634,1.524017499,2.58023862,2.828701275,2.415952799,1.804340365,1.568362423,1.506821068,1.426293562,1.637750712,2.084403813,2.555583789,2.924592632,2.921621967,2.78516253,2.42911631,1.880435523,1.318682187,0.759803269
+0.54884742,0.54884742,0.54884742,0.54884742,0.658616905,2.195389682,4.610318332,6.586169045,9.659714599,3.293084523,5.049396268,5.488474204,4.171240395,4.390779363,4.610318332,5.159165752,4.720087816,7.24478595,7.464324918,7.135016465,6.476399561,5.488474204,2.195389682,1.207464325

data/residential_model/data_residential_by_fuel_end_uses.csv

@@ -1,7 +1,7 @@
-fuel_id,light
-0,100
-1,100
-2,1159
-3,100
-4,100
-5,100
+fuel_id,light,cold,wet,consumer_electronics,home_computing,cooking
+0,0,0,0,0,0,0
+1,0,0,0,0,0,0
+2,1159,1037,1306,1838,549,1138
+3,0,0,0,0,0,0
+4,0,0,0,0,0,0
+5,0,0,0,0,0,0

docs/conf.py

@@ -123,7 +123,7 @@
 
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
-html_theme = 'alabaster'
+html_theme = 'default' #classic, alabaster
 
 # Theme options are theme-specific and customize the look and feel of a theme
 # further.  For a list of options available for each theme, see the

energy_demand/_scrap.py

@@ -0,0 +1,46 @@
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def fnx():
+    return np.random.randint(5, 50, 10)
+
+y = np.row_stack((fnx(), fnx(), fnx()))
+x = np.arange(10)
+
+y1, y2, y3 = fnx(), fnx(), fnx()
+'''
+fig, ax = plt.subplots()
+ax.stackplot(x, y)
+plt.show()
+'''
+fig, ax = plt.subplots()
+
+ax.stackplot(x, y1, y2, y3)
+plt.show()
+
+
+'''import numpy as NP
+from matplotlib import pyplot as PLT
+
+# just create some random data
+fnx = lambda : NP.random.randint(3, 10, 10)
+y = NP.row_stack([fnx(), fnx(), fnx()])   
+# this call to 'cumsum' (cumulative sum), passing in your y data, 
+# is necessary to avoid having to manually order the datasets
+x = NP.arange(10) 
+y_stack = NP.cumsum(y, axis=0)   # a 3x10 array
+
+fig = PLT.figure()
+ax1 = fig.add_subplot(111)
+
+
+
+ax1.fill_between(x, 0, y_stack[0,:], facecolor="green", alpha=1)
+ax1.fill_between(x, y_stack[0,:], y_stack[1,:], facecolor="red", alpha=.4)
+ax1.fill_between(x, y_stack[1,:], y_stack[2,:], facecolor="#6E5160")
+
+PLT.show()
+
+'''

energy_demand/data_loader.py

@@ -17,7 +17,7 @@ def generate_data(data, run_data_collection):
 
     if run_data_collection is False:
         # TODO
-        # Read in dumped csv files
+        # Read in individual CSV files of load shapes
         return data
 
     # --------------
@@ -51,6 +51,4 @@ def generate_data(data, run_data_collection):
     folder_path = r'C:\Users\cenv0553\Dropbox\00-Office_oxford\07-Data\09_Carbon_Trust_advanced_metering_trial_(owen)\__OWN_SEWAGE' #Community _OWN_SEWAGE
     out_dict_av, out_dict_not_av, hourly_shape_of_maximum_days = df.read_raw_carbon_trust_data(data, folder_path)
 
-
-
     return data

energy_demand/data_loader_functions.py

@@ -159,37 +159,32 @@ def get_hes_end_uses_shape(data, hes_data, year_raw_values, hes_y_peak, hes_y_wa
 
 # CWV WEATER GAS SAMSON-----------------------------------
 def shape_residential_heating_gas(data, end_use):
-    """
-    This function creates the shape of the base year heating demand over the full year
-
-    #Todo: Different shapes depending on workingday/holiday
+    """Creates the shape of the base year heating demand over the full year
 
     Input:
     -csv_temp_2015      SNCWV temperatures for every gas-year day
     -hourly_gas_shape   Shape of hourly gas for Day, weekday, weekend (Data from Robert Sansom)
 
     #TODO: THIS CAN BE USED TO DERIVED temp_2015_non_residential_gas data
     """
+    # Initilaise array to store all values for a year
+    year_days, hours = 365, 24
+    hourly_hd = np.zeros((1, hours), dtype=float)
+    hd_data = np.zeros((year_days, hours), dtype=float)
+
 
     # Read in temperatures for base year
     csv_temp_2015 = mf.read_csv(data['path_dict']['path_temp_2015'])
-    hourly_gas_shape = mf.read_csv_float(data['path_dict']['path_hourly_gas_shape']) / 100 #Because given in percentages (division no inlfuence on results as relative anyway)
-    #print("hourly_gas_shape " + str(hourly_gas_shape))
-
     data['csv_temp_2015'] = csv_temp_2015
-    #add hourly data
 
-    # Initilaise array to store all values for a year
-    year_days, hours = 365, 24
-    hourly_hd = np.zeros((1, hours), dtype=float)
-    hd_data = np.zeros((year_days, hours), dtype=float)
-    #shape_hd = np.zeros((year_days, hours), dtype=float)
+    hourly_gas_shape = mf.read_csv_float(data['path_dict']['path_hourly_gas_shape']) / 100 # Because given in percentages (division no inlfuence on results as relative anyway)
+
 
     # Get hourly distribution (Sansom Data)
-    #hourly_gas_shape_day = hourly_gas_shape[0]      # Hourly gas shape
+    #hourly_gas_shape_day = hourly_gas_shape[0]     # Hourly gas shape
     hourly_gas_shape_wkday = hourly_gas_shape[1]    # Hourly gas shape
     hourly_gas_shape_wkend = hourly_gas_shape[2]    # Hourly gas shape
-
+    peak_h_shape = hourly_gas_shape[3]              # Manually derived peak from Robert Sansom
 
     # Read in SNCWV and calculate heating demand for every yearday
     for row in csv_temp_2015:
@@ -247,20 +242,22 @@ def shape_residential_heating_gas(data, end_use):
         cnt += 1
 
     # PEAK-----------------------------------------------------------------------------
-
-
-    # TODO: GET PEAK OF HEATING
-    #print("TEST:")
-    #print(shape_d_non_peak.shape)
-    #prnt("..")
 
+    # Get maximum daily demand
+    _list = []
+    for demand in csv_temp_2015:
+        _list.append(float(demand[1]))
+
+    max_day_demand = max(_list) #maximum demand
 
+    # Factor to calc maximum daily peak #TODO: INclude uncertainty?
+    peak_d_shape = max_day_demand / total_y_hd
 
     # Add to hourly shape
-    data['dict_shapes_end_use_h'][end_use] = {'peak_h_shape': shape_h_non_peak, 'shape_h_non_peak': shape_h_non_peak} # TODO: no peak for gas
+    data['dict_shapes_end_use_h'][end_use] = {'peak_h_shape': peak_h_shape, 'shape_h_non_peak': shape_h_non_peak} # TODO: no peak for gas
 
     # Add to daily shape
-    data['dict_shapes_end_use_d'][end_use]  = {'peak_d_shape': shape_d_non_peak, 'shape_d_non_peak': shape_d_non_peak} # No peak
+    data['dict_shapes_end_use_d'][end_use]  = {'peak_d_shape': peak_d_shape, 'shape_d_non_peak': shape_d_non_peak} # No peak
 
     return data
 

energy_demand/main.py

@@ -16,7 +16,6 @@
 
 - Read out individal load shapes
 - HEating Degree DAys
-- Plot Yearly data
 
 - efficiencies
 - leasitciies
@@ -55,9 +54,6 @@ def energy_demand_model(data, data_ext):
         A nested dictionary containing all data for energy
         supply model with timesteps for every hour in a year.
 
-    Notes
-    -----
-
     """
     # Initialisation
     all_regions = []                                                                                    # List to store all regions
@@ -68,7 +64,6 @@ def energy_demand_model(data, data_ext):
     # Residential model
     # --------------------------
 
-
     # Generate technological stock
     data['tech_stock'] = ts.ResidTechStock(data, data_ext)
 
@@ -119,8 +114,6 @@ def energy_demand_model(data, data_ext):
     # Plot REgion 0 for half a year
     #pf.plot_x_days(result_dict[2], 0, 12)
 
-
-
     return result_dict
 
 # Run

energy_demand/plot_functions.py

@@ -2,6 +2,11 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
+
+
+
+
+
 def plot_x_days(all_hours_year, region, days):
     """With input 2 dim array plot daily load"""
 

energy_demand/residential_model.py

@@ -3,6 +3,7 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
+import matplotlib as mpl
 
 import energy_demand.technological_stock as ts
 import energy_demand.technological_stock_functions as tf
@@ -50,7 +51,7 @@ def __init__(self, reg_id, data, data_ext):
         self.reg_load_factor_h = self.load_factor_h()
 
         # Plot stacked end_uses
-        #self.plot_stacked_regional_end_use()
+        #self.plot_stacked_regional_end_use(1, 2) #days, fueltype
 
     def create_end_use_objects(self):
         """Initialises all defined end uses. Adds an object for each end use to the Region class"""
@@ -180,39 +181,58 @@ def __getattr__subclass__(self, attr_main_class, attr_sub_class):
         object_subclass = getattr(object_class, attr_sub_class)
         return object_subclass
 
+    def plot_stacked_regional_end_use(self, nr_of_day_to_plot, fueltype):
+        """ Plots stacked end_use for a region
+        #TODO: Make that end_uses can be sorted, improve legend...
+        """
 
-    '''def plot_stacked_regional_end_use(self):
-        """ PLot End Uses of Region """
+        fig, ax = plt.subplots()
+        nr_hours_to_plot = nr_of_day_to_plot * 24
 
-        nr_of_day_to_plot = 10
-        fueltype = 2
+        x = np.arange(nr_hours_to_plot)
+        legend_entries = []
 
-        x_values = range(nr_of_day_to_plot * 24)
-        
+        # Initialise (number of enduses, number of hours to plot)
+        Y_init = np.zeros((len(self.reg_fuel), nr_hours_to_plot))
 
-        # Iterate End_uses
-        for enduse in self.reg_fuel:
-            y_values = []
+        # Iterate enduse
+        for k, enduse in enumerate(self.reg_fuel):
+            legend_entries.append(enduse)
+            sum_fuels_h = self.__getattr__subclass__(enduse, 'enduse_fuel_h') #np.around(fuel_end_use_h,10)
+            data_fueltype_enduse = np.zeros((nr_hours_to_plot, ))
+            list_all_h = []
+
+            #Get data of a fueltype
+            for j, fuel_data in enumerate(sum_fuels_h[fueltype][:nr_of_day_to_plot]):
+
+                for h in fuel_data:
+                    list_all_h.append(h)
 
-            print(" ENDUSE: " + str(enduse))
-            day_all_fuels = self.__getattr__subclass__(enduse, 'enduse_fuel_h')
+            Y_init[k] = list_all_h
 
-            for day, d_value in enumerate(day_all_fuels[fueltype]):
+        color_list = ["green", "red", "#6E5160"]
 
-                # ONLY PLOT HALF A YEAR
-                if day < nr_of_day_to_plot:
+        sp = ax.stackplot(x,Y_init)
+        proxy = [mpl.patches.Rectangle((0,0), 0,0, facecolor=pol.get_facecolor()[0]) for pol in sp]
 
-                    for h in d_value:
-                        y_values.append(h)
+        ax.legend(proxy, legend_entries)
 
-            plt.plot(x_values, y_values)
+        plt.axis('tight')
 
         plt.xlabel("Hours")
         plt.ylabel("Energy demand in kWh")
-        plt.title("Energy Demand")
-        plt.legend()
+        plt.title("Stacked energy demand")
+
+        #from matplotlib.patches import Rectangle
+
+        #legend_boxes = []
+        #for i in color_list:
+        #    box = Rectangle((0, 0), 1, 1, fc=i)
+        #    legend_boxes.append(box)
+        #ax.legend(legend_boxes, legend_entries)
+
+        #ax.stackplot(x, Y_init)
         plt.show()
-        '''
 
 
 class EndUseClassResid(Region):