P2_preprocessing.ipynb

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      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import holidays as hd\n",
        "\n",
        "from time import time\n",
        "import datetime\n",
        "import random\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading Data"
      ],
      "metadata": {
        "id": "Hr5GHO7hA1kP"
      },
      "id": "Hr5GHO7hA1kP"
    },
    {
      "cell_type": "code",
      "source": [
        "# Set main working directory and secondary folder paths\n",
        "directory = os.getcwd() # get working directory\n",
        "data_path = directory + \"/data/\"\n",
        "models_path = directory + \"/models/\""
      ],
      "metadata": {
        "id": "dvnw095j4QQR"
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      "id": "dvnw095j4QQR",
      "execution_count": null,
      "outputs": []
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          "text": [
            "<ipython-input-70-3c3c69d1651e>:2: DtypeWarning: Columns (1,2,3,4,5,6,7,8) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  weather = pd.read_csv(data_path+'weather'+'.csv')\n"
          ]
        }
      ],
      "source": [
        "# Read the relevant weather data from the weather.csv file\n",
        "weather = pd.read_csv(data_path+'weather'+'.csv')\n",
        "weather.columns = weather.iloc[1]\n",
        "weather = weather.iloc[2:]\n",
        "weather['date'] = pd.to_datetime(weather['time']) # convert timestamps to dates\n",
        "weather = weather.loc[weather['date'] >= '2015-01-01 00:00:00'] # keep data from 2015 onwards\n",
        "weather.index = weather['date'] # set the date as the index\n",
        "weather = weather[['temperature', 'irradiance_surface', 'precipitation', 'cloud_cover']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d77b7aee",
      "metadata": {
        "id": "d77b7aee"
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      "source": [
        "# Read the relevant consumption data from the consumption.csv file\n",
        "consumption = pd.read_csv(data_path+'consumption'+'.csv')\n",
        "consumption['date'] = pd.to_datetime(consumption['utc_timestamp'], format='%Y-%m-%dT%H:%M:%SZ') # convert timestamps to dates\n",
        "consumption.index = consumption['date'] # set the date as the index\n",
        "consumption = pd.DataFrame(consumption['ES_load_actual_entsoe_transparency'][1:])\n",
        "consumption.columns = ['load']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# combine consumption and weather data into a single dataframe\n",
        "data = pd.concat([consumption, weather], axis=1)\n",
        "data"
      ],
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            "text/plain": [
              "                        load temperature irradiance_surface precipitation  \\\n",
              "date                                                                        \n",
              "2015-01-01 00:00:00      NaN       3.438                0.0        0.0002   \n",
              "2015-01-01 01:00:00  22734.0       3.217                0.0        0.0003   \n",
              "2015-01-01 02:00:00  21286.0       3.103                0.0        0.0004   \n",
              "2015-01-01 03:00:00  20264.0       3.051                0.0        0.0004   \n",
              "2015-01-01 04:00:00  19905.0       2.982                0.0        0.0003   \n",
              "...                      ...         ...                ...           ...   \n",
              "2019-12-31 19:00:00  29362.0       8.295                0.0        0.0001   \n",
              "2019-12-31 20:00:00  27608.0       7.805                0.0        0.0001   \n",
              "2019-12-31 21:00:00  25241.0       7.342                0.0        0.0003   \n",
              "2019-12-31 22:00:00  23911.0       6.959                0.0        0.0003   \n",
              "2019-12-31 23:00:00      NaN       6.652                0.0        0.0003   \n",
              "\n",
              "                    cloud_cover  \n",
              "date                             \n",
              "2015-01-01 00:00:00      0.1066  \n",
              "2015-01-01 01:00:00      0.1254  \n",
              "2015-01-01 02:00:00       0.121  \n",
              "2015-01-01 03:00:00       0.095  \n",
              "2015-01-01 04:00:00       0.083  \n",
              "...                         ...  \n",
              "2019-12-31 19:00:00      0.1557  \n",
              "2019-12-31 20:00:00      0.1382  \n",
              "2019-12-31 21:00:00      0.1363  \n",
              "2019-12-31 22:00:00      0.1233  \n",
              "2019-12-31 23:00:00      0.1254  \n",
              "\n",
              "[43824 rows x 5 columns]"
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>load</th>\n",
              "      <th>temperature</th>\n",
              "      <th>irradiance_surface</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>cloud_cover</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>2015-01-01 00:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3.438</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0002</td>\n",
              "      <td>0.1066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-01 01:00:00</th>\n",
              "      <td>22734.0</td>\n",
              "      <td>3.217</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>0.1254</td>\n",
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              "    <tr>\n",
              "      <th>2015-01-01 02:00:00</th>\n",
              "      <td>21286.0</td>\n",
              "      <td>3.103</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0004</td>\n",
              "      <td>0.121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-01 03:00:00</th>\n",
              "      <td>20264.0</td>\n",
              "      <td>3.051</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0004</td>\n",
              "      <td>0.095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-01 04:00:00</th>\n",
              "      <td>19905.0</td>\n",
              "      <td>2.982</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>0.083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31 19:00:00</th>\n",
              "      <td>29362.0</td>\n",
              "      <td>8.295</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>0.1557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31 20:00:00</th>\n",
              "      <td>27608.0</td>\n",
              "      <td>7.805</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>0.1382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31 21:00:00</th>\n",
              "      <td>25241.0</td>\n",
              "      <td>7.342</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>0.1363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31 22:00:00</th>\n",
              "      <td>23911.0</td>\n",
              "      <td>6.959</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>0.1233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31 23:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>6.652</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>0.1254</td>\n",
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              "<p>43824 rows × 5 columns</p>\n",
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    {
      "cell_type": "code",
      "source": [
        "# Look for NaN values\n",
        "print(f'A total of {data.isnull().values.sum()} NaN values:\\n')\n",
        "data.index[data.isnull().any(axis=1)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yjl6bE6SPNwn",
        "outputId": "a6e1ec65-a775-4257-e145-12df643f0578"
      },
      "id": "yjl6bE6SPNwn",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A total of 19 NaN values:\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2015-01-01 00:00:00', '2015-01-02 06:00:00',\n",
              "               '2015-01-02 07:00:00', '2015-01-02 08:00:00',\n",
              "               '2015-01-02 11:00:00', '2015-01-02 12:00:00',\n",
              "               '2015-01-02 13:00:00', '2015-01-02 14:00:00',\n",
              "               '2015-01-02 15:00:00', '2015-01-02 16:00:00',\n",
              "               '2015-01-02 17:00:00', '2015-01-02 18:00:00',\n",
              "               '2015-05-01 10:00:00', '2015-05-01 11:00:00',\n",
              "               '2015-05-01 12:00:00', '2015-05-01 13:00:00',\n",
              "               '2015-05-01 14:00:00', '2015-05-01 15:00:00',\n",
              "               '2019-12-31 23:00:00'],\n",
              "              dtype='datetime64[ns]', name='date', freq=None)"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def fill_nan(df):\n",
        "  \"\"\"\n",
        "  Replaces NaN values by using the forward and backward fill method.\n",
        "\n",
        "  Args\n",
        "  ---------\n",
        "  pd.DataFrame df: the original dataframe with NaN values\n",
        "\n",
        "  Returns\n",
        "  -------\n",
        "  pd.DataFrame df: the cleaned dataframe\n",
        "  \"\"\"\n",
        "  df.fillna(method='ffill', inplace=True)\n",
        "  df.fillna(method='bfill', inplace=True)\n",
        "  return df"
      ],
      "metadata": {
        "id": "qCsany_F4kS7"
      },
      "id": "qCsany_F4kS7",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fix NaN values\n",
        "data = fill_nan(data)"
      ],
      "metadata": {
        "id": "vX0uQfIY4x38"
      },
      "id": "vX0uQfIY4x38",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "legends = {\n",
        "    0: 'Load (MW)',\n",
        "    1: 'Temperature (C)',\n",
        "    2: 'Irradiance (W/m^2)',\n",
        "    3: 'Precipitation (mm/hour)',\n",
        "    4: 'Cloud Cover [0,1]'\n",
        "}\n",
        "\n",
        "colors = sns.color_palette()\n",
        "sns.set_theme(style='darkgrid')"
      ],
      "metadata": {
        "id": "rHTi9MzzIhYc"
      },
      "id": "rHTi9MzzIhYc",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocessing"
      ],
      "metadata": {
        "id": "EkN4GnOeBPAr"
      },
      "id": "EkN4GnOeBPAr"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Z-score outlier detection"
      ],
      "metadata": {
        "id": "qdjp28UlUll1"
      },
      "id": "qdjp28UlUll1"
    },
    {
      "cell_type": "code",
      "source": [
        "def find_outliers(df):\n",
        "  \"\"\"\n",
        "  Finds and plots outliers on each feature of a dataframe.\n",
        "\n",
        "  Args\n",
        "  ---------\n",
        "  pd.DataFrame df: the original dataframe\n",
        "  \"\"\"\n",
        "  # Create as many sublplots as the features\n",
        "  fig, axes = plt.subplots(nrows=1, ncols=len(df.columns), figsize=(15, 5))\n",
        "\n",
        "  # for every feature of the data set\n",
        "  for i, feature in enumerate(df.columns):\n",
        "    d = df[feature]\n",
        "\n",
        "    # calculate the Z-score of each data point\n",
        "    z_scores = (d - d.mean()) / d.std()\n",
        "\n",
        "    # identify any points with a Z-score > 3\n",
        "    outliers = d[abs(z_scores) > 3]\n",
        "\n",
        "    # create plot showing if there are any outliers\n",
        "    sns.violinplot(data=z_scores, ax=axes[i], color=colors[i])\n",
        "\n",
        "    title = 'No outliers'\n",
        "    if len(outliers) > 0:\n",
        "      title = f'{len(outliers)} outliers'\n",
        "\n",
        "    axes[i].set_title(feature.capitalize())\n",
        "    axes[i].set_ylabel('z-score')\n",
        "    axes[i].set_xlabel(title)\n",
        "    axes[i].set_xticklabels('')\n",
        "\n",
        "  fig.suptitle(\"Outlier Detection ( |Z-Score| > 3 )\\n\", fontsize=14)\n",
        "  plt.tight_layout()\n",
        "  plt.show()\n",
        "  return\n",
        "\n",
        "colors = sns.color_palette()\n",
        "sns.set_theme(style='darkgrid')\n",
        "find_outliers(data)"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "6f30357a-2736-4eea-f64a-7f0f2e0670f7"
      },
      "id": "3-JDgvXJUf0a",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Augmentation"
      ],
      "metadata": {
        "id": "d5rYVTiHUtvR"
      },
      "id": "d5rYVTiHUtvR"
    },
    {
      "cell_type": "code",
      "source": [
        "# create a one-hot encoded day category dataframe\n",
        "weekday_map = {'Monday':    'mon',\n",
        "               'Tuesday':   'wkd',\n",
        "               'Wednesday': 'wkd',\n",
        "               'Thursday':  'wkd',\n",
        "               'Friday':    'fri',\n",
        "               'Saturday':  'wkn',\n",
        "               'Sunday':    'wkn'}\n",
        "\n",
        "day_cat = pd.get_dummies(data.index.day_name().map(weekday_map))[['mon', 'wkd', 'fri', 'wkn']]\n",
        "day_cat"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KqzRxP6k3Dml",
        "outputId": "2cfe67cf-5b15-402f-f63a-a4beed205366"
      },
      "id": "KqzRxP6k3Dml",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       mon  wkd  fri  wkn\n",
              "0        0    1    0    0\n",
              "1        0    1    0    0\n",
              "2        0    1    0    0\n",
              "3        0    1    0    0\n",
              "4        0    1    0    0\n",
              "...    ...  ...  ...  ...\n",
              "43819    0    1    0    0\n",
              "43820    0    1    0    0\n",
              "43821    0    1    0    0\n",
              "43822    0    1    0    0\n",
              "43823    0    1    0    0\n",
              "\n",
              "[43824 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e4591d71-2007-4b98-bb0d-8970baff484c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mon</th>\n",
              "      <th>wkd</th>\n",
              "      <th>fri</th>\n",
              "      <th>wkn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43819</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43820</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43821</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43822</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43823</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>43824 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e4591d71-2007-4b98-bb0d-8970baff484c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-e4591d71-2007-4b98-bb0d-8970baff484c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e4591d71-2007-4b98-bb0d-8970baff484c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # retrieve holiday list dictionary from the top-4 most populated provinces\n",
        "years = range(2015, 2020)\n",
        "provinces = ['MD', 'AN', 'CT', 'VC'] # [Madrid, Andalusia, Catalonia, Valencia]\n",
        "holiday_list = hd.ES(years=years, prov=provinces[0])\n",
        "for province in provinces:\n",
        "  holiday_list.update(hd.ES(years=years, prov=province))\n",
        "\n",
        "print(f'Number of total holidays: {len(holiday_list)}')\n",
        "\n",
        "# create binary dataframe with value=1 if the day is a holiday\n",
        "holidays = pd.DataFrame(data.index)\n",
        "holidays['hld'] = 0\n",
        "holidays.loc[holidays['date'].apply(lambda d: d in holiday_list), 'hld'] = 1\n",
        "holidays = pd.DataFrame(holidays['hld'])\n",
        "holidays.index = data.index\n",
        "holidays"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MQ0wBpf-3hm7",
        "outputId": "1bdd1d7f-f256-44a6-beb6-d48f8a52b6b1"
      },
      "id": "MQ0wBpf-3hm7",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of total holidays: 92\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     hld\n",
              "date                    \n",
              "2015-01-01 00:00:00    1\n",
              "2015-01-01 01:00:00    1\n",
              "2015-01-01 02:00:00    1\n",
              "2015-01-01 03:00:00    1\n",
              "2015-01-01 04:00:00    1\n",
              "...                  ...\n",
              "2019-12-31 19:00:00    0\n",
              "2019-12-31 20:00:00    0\n",
              "2019-12-31 21:00:00    0\n",
              "2019-12-31 22:00:00    0\n",
              "2019-12-31 23:00:00    0\n",
              "\n",
              "[43824 rows x 1 columns]"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hld</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-01-01 00:00:00</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-01 01:00:00</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-01 02:00:00</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-01 03:00:00</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-01 04:00:00</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2019-12-31 19:00:00</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31 20:00:00</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31 21:00:00</th>\n",
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              "    <tr>\n",
              "      <th>2019-12-31 22:00:00</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2019-12-31 23:00:00</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>43824 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3d77f258-50b4-4c2f-8c0b-46db8f966ae4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3d77f258-50b4-4c2f-8c0b-46db8f966ae4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Transformation"
      ],
      "metadata": {
        "id": "1F47w44TUpVq"
      },
      "id": "1F47w44TUpVq"
    },
    {
      "cell_type": "code",
      "source": [
        "# convert load from MW to GW\n",
        "data['load'] /= 1000.0"
      ],
      "metadata": {
        "id": "Ml3RCujNB-SB"
      },
      "id": "Ml3RCujNB-SB",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use Standard Scaliner to normalize all the unique data features\n",
        "scalers = {}\n",
        "\n",
        "for feature in data.columns:\n",
        "  scaler = StandardScaler()\n",
        "  data[feature] = scaler.fit_transform(np.array(data[feature]).reshape(-1, 1))\n",
        "  scalers.update({feature: scaler})"
      ],
      "metadata": {
        "id": "pBaloyjwE-Rq"
      },
      "id": "pBaloyjwE-Rq",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MA1jaEaKGg-s",
        "outputId": "81e3efd3-d966-4d0e-97c0-7165b9fb666d"
      },
      "id": "MA1jaEaKGg-s",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               load   temperature  irradiance_surface  precipitation  \\\n",
              "count  4.382400e+04  4.382400e+04        4.382400e+04   4.382400e+04   \n",
              "mean   4.864066e-16  3.320536e-16       -2.594168e-17  -6.744838e-17   \n",
              "std    1.000011e+00  1.000011e+00        1.000011e+00   1.000011e+00   \n",
              "min   -2.518242e+00 -2.283490e+00       -7.421028e-01  -5.498245e-01   \n",
              "25%   -8.471838e-01 -8.177143e-01       -7.421028e-01  -5.273822e-01   \n",
              "50%    4.471504e-02 -1.044044e-01       -6.896978e-01  -4.056754e-01   \n",
              "75%    7.615632e-01  7.168984e-01        6.509381e-01   6.043541e-02   \n",
              "max    2.704697e+00  2.733647e+00        2.845695e+00   1.611623e+01   \n",
              "\n",
              "        cloud_cover  \n",
              "count  4.382400e+04  \n",
              "mean  -1.219259e-16  \n",
              "std    1.000011e+00  \n",
              "min   -1.687913e+00  \n",
              "25%   -8.391267e-01  \n",
              "50%   -1.042268e-01  \n",
              "75%    7.755073e-01  \n",
              "max    2.320819e+00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5b7a240b-a436-4359-9abb-74cb5efbd0d2\">\n",
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              "      <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>load</th>\n",
              "      <th>temperature</th>\n",
              "      <th>irradiance_surface</th>\n",
              "      <th>precipitation</th>\n",
              "      <th>cloud_cover</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.382400e+04</td>\n",
              "      <td>4.382400e+04</td>\n",
              "      <td>4.382400e+04</td>\n",
              "      <td>4.382400e+04</td>\n",
              "      <td>4.382400e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.864066e-16</td>\n",
              "      <td>3.320536e-16</td>\n",
              "      <td>-2.594168e-17</td>\n",
              "      <td>-6.744838e-17</td>\n",
              "      <td>-1.219259e-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.000011e+00</td>\n",
              "      <td>1.000011e+00</td>\n",
              "      <td>1.000011e+00</td>\n",
              "      <td>1.000011e+00</td>\n",
              "      <td>1.000011e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-2.518242e+00</td>\n",
              "      <td>-2.283490e+00</td>\n",
              "      <td>-7.421028e-01</td>\n",
              "      <td>-5.498245e-01</td>\n",
              "      <td>-1.687913e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-8.471838e-01</td>\n",
              "      <td>-8.177143e-01</td>\n",
              "      <td>-7.421028e-01</td>\n",
              "      <td>-5.273822e-01</td>\n",
              "      <td>-8.391267e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.471504e-02</td>\n",
              "      <td>-1.044044e-01</td>\n",
              "      <td>-6.896978e-01</td>\n",
              "      <td>-4.056754e-01</td>\n",
              "      <td>-1.042268e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.615632e-01</td>\n",
              "      <td>7.168984e-01</td>\n",
              "      <td>6.509381e-01</td>\n",
              "      <td>6.043541e-02</td>\n",
              "      <td>7.755073e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.704697e+00</td>\n",
              "      <td>2.733647e+00</td>\n",
              "      <td>2.845695e+00</td>\n",
              "      <td>1.611623e+01</td>\n",
              "      <td>2.320819e+00</td>\n",
              "    </tr>\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Extraction"
      ],
      "metadata": {
        "id": "pxKVxCYmbhF2"
      },
      "id": "pxKVxCYmbhF2"
    },
    {
      "cell_type": "markdown",
      "source": [
        "```\n",
        "[ X data ]\n",
        "--------------------------------\n",
        "H1. Consumption   Nd X 24h\n",
        "H2. Temperature   Nd X 24h\n",
        "H3. Irradiance    Nd X 24h\n",
        "H4. Precipitation Nd X 24h\n",
        "H5. Cloud Cover   Nd X 24h\n",
        "\n",
        "F1. Temperature Forecast   24h\n",
        "F2. Irradiance Forecast    24h\n",
        "F3. Precipitation Forecast 24h\n",
        "F4. Cloud Cover Forecast   24h\n",
        "\n",
        "C1. Day-ahead Day Number    1\n",
        "C2. Day-ahead Month Number  1\n",
        "C3. Day-ahead Day Category  4\n",
        "C4. Day-ahead Holiday       1\n",
        "\n",
        "[ Y data ]\n",
        "--------------------------------\n",
        "Y1. Consumption Forecast 24h\n",
        "```"
      ],
      "metadata": {
        "id": "dOHKUaNLEwWc"
      },
      "id": "dOHKUaNLEwWc"
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_features(days_back, data, day_cat, holidays):\n",
        "  \"\"\"\n",
        "  Calculates feature vectors, specified by the days_back properties\n",
        "  dictionary and returns the X and Y arrays with the preprocessed data.\n",
        "\n",
        "  Args\n",
        "  ---------\n",
        "  dict days_back: properties for days_back count to be considered for feature vectors\n",
        "  pd.DataFrame data: concatenated consumption and weather data features\n",
        "  pd.DataFrame day_cat: one-hot encoded day category dataframe\n",
        "  pd.DataFrame holidays: binary holiday indicator dataframe\n",
        "\n",
        "  Returns\n",
        "  -------\n",
        "  np.ndarray X: shape=(n_days, n_features), represents input x data\n",
        "  np.ndarray Y: shape=(n_days, 24), represents target y data\n",
        "  \"\"\"\n",
        "  X = []\n",
        "  Y = []\n",
        "\n",
        "  first_black_day = max(days_back.values()) # find the first black day\n",
        "\n",
        "  # iterate through all black days\n",
        "  for day_id in range(first_black_day, (data.shape[0]-24)//24):\n",
        "\n",
        "    # Calculate historical load and weather features\n",
        "    H1 = data['load'].iloc[(day_id-days_back['load'])*24:(day_id)*24]\n",
        "    H2 = data['temperature'].iloc[(day_id-days_back['temperature'])*24:(day_id)*24]\n",
        "    H3 = data['irradiance_surface'].iloc[(day_id-days_back['irradiance_surface'])*24:(day_id)*24]\n",
        "    H4 = data['precipitation'].iloc[(day_id-days_back['precipitation'])*24:(day_id)*24]\n",
        "    H5 = data['cloud_cover'].iloc[(day_id-days_back['cloud_cover'])*24:(day_id)*24]\n",
        "\n",
        "    next24h = range((day_id)*24, (day_id+1)*24) # calculate the range for the next 24h (used for weather forecasts)\n",
        "    next1d = (day_id+1)*24 # the id of the next day\n",
        "\n",
        "    # Calculate simulated day-ahead weather forecast\n",
        "    F1 = data['temperature'].iloc[next24h]\n",
        "    F2 = data['irradiance_surface'].iloc[next24h]\n",
        "    F3 = data['precipitation'].iloc[next24h]\n",
        "    F4 = data['cloud_cover'].iloc[next24h]\n",
        "\n",
        "    # Retrieve day-ahead calendar features\n",
        "    C1 = int(data.index.day[next1d])\n",
        "    C2 = int(data.index.month[next1d])\n",
        "    C3 = np.array(day_cat.iloc[next1d])\n",
        "    C4 = int(holidays.iloc[next1d])\n",
        "    calendar = np.concatenate((np.array([C1, C2]), C3, np.array([C4])))\n",
        "\n",
        "    # Create the X feature vector by combining all of the above\n",
        "    feature_vec = np.concatenate((H1, H2, H3, H4, H5, F1, F2, F3, F4, calendar))\n",
        "    X.append(feature_vec)\n",
        "\n",
        "    # Create the Y feature vector by retrieving the actual day-ahead load values\n",
        "    Y1 = data['load'].iloc[(day_id+1)*24:(day_id+2)*24]\n",
        "    Y.append(np.array(Y1))\n",
        "\n",
        "  return np.array(X, dtype='float64'), np.array(Y, dtype='float64')"
      ],
      "metadata": {
        "id": "epEZMvbiEHjK"
      },
      "id": "epEZMvbiEHjK",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def split_train_test_val(A):\n",
        "  \"\"\"\n",
        "  Splits data into train, validation and test sets.\n",
        "\n",
        "  Args\n",
        "  ---------\n",
        "  np.ndarray A: shape=(n_days, n_features) feature vector table\n",
        "\n",
        "  Returns\n",
        "  -------\n",
        "  np.ndarray train: the train set\n",
        "  np.ndarray val:   the validation set\n",
        "  np.ndarray test:  the test set\n",
        "  \"\"\"\n",
        "\n",
        "  n_instances = A.shape[0]\n",
        "\n",
        "  n_val = 365  # Keep a year's worth of validation data\n",
        "  n_test = 365 # Keep a year's worth of testing data\n",
        "  n_train = n_instances - n_val - n_test # Leave the rest for training\n",
        "\n",
        "  indices = range(A.shape[0])\n",
        "\n",
        "  train = A[indices[:n_train]] # Years 2015-2017: training data\n",
        "  val   = A[indices[n_train:n_train+n_val]] # Year 2018: validation data\n",
        "  test  = A[indices[n_train+n_val:]] # Year 2019: testing data\n",
        "\n",
        "  return train, val, test"
      ],
      "metadata": {
        "id": "xhQEaSGHY8ez"
      },
      "id": "xhQEaSGHY8ez",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# specifies the days_back amount that will be considered for each historical feature value (H1-H5)\n",
        "days_back = {\n",
        "    'load': 7,\n",
        "    'temperature': 7,\n",
        "    'irradiance_surface': 7,\n",
        "    'precipitation': 7,\n",
        "    'cloud_cover': 7,\n",
        "}"
      ],
      "metadata": {
        "id": "QtNXE8U9B89V"
      },
      "id": "QtNXE8U9B89V",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# extract features and print shapes\n",
        "X, Y = extract_features(days_back, data, day_cat, holidays)\n",
        "n_features = X.shape[1]\n",
        "print(X.shape, Y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OrM35sPaEHD7",
        "outputId": "17439265-2c10-440d-e38c-d147627cf455"
      },
      "id": "OrM35sPaEHD7",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1818, 943) (1818, 24)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split the data into train, validation and test sets\n",
        "x_train, x_val, x_test = split_train_test_val(X)\n",
        "y_train, y_val, y_test = split_train_test_val(Y)"
      ],
      "metadata": {
        "id": "t6C-Y_8eZX7x"
      },
      "id": "t6C-Y_8eZX7x",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# confirm that the shapes are as needed\n",
        "print(x_train.shape, x_val.shape, x_test.shape)\n",
        "print(y_train.shape, y_val.shape, y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jzMcUv8LZseK",
        "outputId": "c913c989-0c87-4175-c11c-623471308505"
      },
      "id": "jzMcUv8LZseK",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1088, 943) (365, 943) (365, 943)\n",
            "(1088, 24) (365, 24) (365, 24)\n"
          ]
        }
      ]
    }
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