Modelling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cab2a35b-83b1-4c29-973e-ace5251ed8bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import pandas as pd\n",
    "import glob\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from matplotlib.lines import Line2D\n",
    "from itertools import product\n",
    "# For seasonal decomposition\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose, STL\n",
    "# Import additional functions needed\n",
    "from statsmodels.tsa.stattools import adfuller, kpss\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "# to measure model performance\n",
    "from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error, r2_score\n",
    "# To model with sklearn\n",
    "from sklearn.linear_model import Lasso, Ridge, LinearRegression, HuberRegressor, ElasticNet\n",
    "from sklearn.utils.validation import check_is_fitted\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "89fd52b9-508e-426c-a100-d8472bee9149",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>windspeed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01 00:00:00</th>\n",
       "      <td>19.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 01:00:00</th>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 02:00:00</th>\n",
       "      <td>17.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 03:00:00</th>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 04:00:00</th>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 07:00:00</th>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 08:00:00</th>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 09:00:00</th>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 10:00:00</th>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 11:00:00</th>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>122844 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     windspeed\n",
       "datetime                      \n",
       "2010-01-01 00:00:00       19.4\n",
       "2010-01-01 01:00:00       21.8\n",
       "2010-01-01 02:00:00       17.7\n",
       "2010-01-01 03:00:00       18.0\n",
       "2010-01-01 04:00:00       17.4\n",
       "...                        ...\n",
       "2024-01-06 07:00:00        9.6\n",
       "2024-01-06 08:00:00        8.9\n",
       "2024-01-06 09:00:00        9.6\n",
       "2024-01-06 10:00:00        7.8\n",
       "2024-01-06 11:00:00       16.0\n",
       "\n",
       "[122844 rows x 1 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading the csv file that we exported after exploration for the deployment of ML models\n",
    "df_windspeed=pd.read_csv('windspeed_data.csv')\n",
    "df_windspeed.rename(columns={'Unnamed: 0': 'datetime'}, inplace=True)\n",
    "df_windspeed['datetime'] = pd.to_datetime(df_windspeed['datetime'])\n",
    "df_windspeed.set_index('datetime', inplace=True)\n",
    "df_windspeed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e8ff784-7865-4888-b7d3-05ace412980e",
   "metadata": {},
   "source": [
    "## Basline Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f3cb38ee-ce73-42ec-b6f6-15f96dfb64a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start and end of training set:\t\t 2010-01-01 00:00:00 2020-12-31 23:00:00\n",
      "Start and end of validation set:\t 2021-01-01 00:00:00 2022-12-31 23:00:00\n",
      "Start and end of test set:\t\t 2023-01-01 00:00:00 2023-12-31 23:00:00\n"
     ]
    }
   ],
   "source": [
    "#Split the data in train, validation and test (2010-2020=Train, 2021-2022=Valdation, 2023=Test)\n",
    "#For dummy/Base line I am using the monthly data as it becomes easy to visualize\n",
    "# Split the dataset\n",
    "train = df_windspeed['2010':'2020']  \n",
    "validation = df_windspeed.loc['2021':'2022'] \n",
    "test = df_windspeed.loc['2023']  \n",
    "print('Start and end of training set:\\t\\t', train.index.min(), train.index.max())\n",
    "print('Start and end of validation set:\\t', validation.index.min(), validation.index.max())\n",
    "print('Start and end of test set:\\t\\t', test.index.min(), test.index.max())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "467df320-5e7a-44b1-9c2a-5015d79c4df9",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the train, valdation and test\n",
    "fig, ax = plt.subplots(figsize=(16,8))\n",
    "\n",
    "# Plot windspeed data\n",
    "ax.plot(df_windspeed.index, df_windspeed['windspeed'])\n",
    "\n",
    "# Highlight validation and test sets\n",
    "ax.axvspan(validation.index.min(), validation.index.max(), alpha=.2, color='red')  # validation set\n",
    "ax.axvspan(test.index.min(), test.index.max(), alpha=.2, color='blue')  # test set\n",
    "\n",
    "# Set labels and limits\n",
    "ax.set_xlabel('Time (year)')\n",
    "ax.set_ylabel('windspeed')\n",
    "ax.set_xlim(train.index.min(), test.index.max())\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "55e56005-9722-4e30-a287-4eea76710d14",
   "metadata": {},
   "outputs": [],
   "source": [
    "def measure_forecast_error(label, forecast):\n",
    "    '''We will use this function to measure the performance of our baseline models'''\n",
    "    print('MAE = %.2f' % mean_absolute_error(label, forecast))\n",
    "    print('MAPE = %.2f%%' % (100*mean_absolute_percentage_error(label, forecast)))\n",
    "    print('R2 = %.2f' % r2_score(label, forecast))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9bad1215-6d92-4003-9616-f1e87233da11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE = 9.24\n",
      "MAPE = 311036275634703.88%\n",
      "R2 = -0.90\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The dummy basline is used where the data from the 2019-2022 is used to forcast\n",
    "validation['baseline_last_period'] = train.loc['2019':'2020']['windspeed'].values[:len(validation)]\n",
    "\n",
    "measure_forecast_error(validation['windspeed'], validation['baseline_last_period'])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16,8))\n",
    "ax.plot(df_windspeed.index, df_windspeed['windspeed'], label='True value')\n",
    "ax.plot(validation.index, validation['baseline_last_period'], '--', color='k', label='Forecast')\n",
    "ax.axvspan(validation.index.min(), validation.index.max(), alpha=.2, color='red')  # validation set\n",
    "ax.axvspan(test.index.min(), test.index.max(), alpha=.2, color='blue')  # test set\n",
    "ax.set_xlabel('Time (year)')\n",
    "ax.set_ylabel('Windspeed')\n",
    "_=ax.set_xlim(train.index.min(), test.index.max())\n",
    "plt.legend(loc='best')\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1c3964f7-4108-4e0b-a2b1-b3ec082a72aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE = 6.89\n",
      "MAPE = 411907189534038.94%\n",
      "R2 = -0.01\n"
     ]
    }
   ],
   "source": [
    "#the dummy from previous data does not gives good results here i tried the mean of past data however no much improvement was there\n",
    "validation['baseline_mean'] = train['windspeed'].mean()\n",
    "\n",
    "measure_forecast_error(validation['windspeed'], validation['baseline_mean'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3814175f-9a5c-44ce-a70b-a059232e49c4",
   "metadata": {},
   "source": [
    "## Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7f646068-831a-42be-acf5-8829175a5b0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>windspeed</th>\n",
       "      <th>Windspeed_lag1</th>\n",
       "      <th>Windspeed_lag2</th>\n",
       "      <th>Windspeed_lag3</th>\n",
       "      <th>Windspeed_lag4</th>\n",
       "      <th>Windspeed_lag5</th>\n",
       "      <th>Windspeed_lag6</th>\n",
       "      <th>Windspeed_lag7</th>\n",
       "      <th>Windspeed_lag8</th>\n",
       "      <th>Windspeed_lag9</th>\n",
       "      <th>...</th>\n",
       "      <th>Windspeed_lag15</th>\n",
       "      <th>Windspeed_lag16</th>\n",
       "      <th>Windspeed_lag17</th>\n",
       "      <th>Windspeed_lag18</th>\n",
       "      <th>Windspeed_lag19</th>\n",
       "      <th>Windspeed_lag20</th>\n",
       "      <th>Windspeed_lag21</th>\n",
       "      <th>Windspeed_lag22</th>\n",
       "      <th>Windspeed_lag23</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01 00:00:00</th>\n",
       "      <td>19.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 01:00:00</th>\n",
       "      <td>21.8</td>\n",
       "      <td>19.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 02:00:00</th>\n",
       "      <td>17.7</td>\n",
       "      <td>21.8</td>\n",
       "      <td>19.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 03:00:00</th>\n",
       "      <td>18.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>21.8</td>\n",
       "      <td>19.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 04:00:00</th>\n",
       "      <td>17.4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>21.8</td>\n",
       "      <td>19.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     windspeed  Windspeed_lag1  Windspeed_lag2  \\\n",
       "datetime                                                         \n",
       "2010-01-01 00:00:00       19.4             NaN             NaN   \n",
       "2010-01-01 01:00:00       21.8            19.4             NaN   \n",
       "2010-01-01 02:00:00       17.7            21.8            19.4   \n",
       "2010-01-01 03:00:00       18.0            17.7            21.8   \n",
       "2010-01-01 04:00:00       17.4            18.0            17.7   \n",
       "\n",
       "                     Windspeed_lag3  Windspeed_lag4  Windspeed_lag5  \\\n",
       "datetime                                                              \n",
       "2010-01-01 00:00:00             NaN             NaN             NaN   \n",
       "2010-01-01 01:00:00             NaN             NaN             NaN   \n",
       "2010-01-01 02:00:00             NaN             NaN             NaN   \n",
       "2010-01-01 03:00:00            19.4             NaN             NaN   \n",
       "2010-01-01 04:00:00            21.8            19.4             NaN   \n",
       "\n",
       "                     Windspeed_lag6  Windspeed_lag7  Windspeed_lag8  \\\n",
       "datetime                                                              \n",
       "2010-01-01 00:00:00             NaN             NaN             NaN   \n",
       "2010-01-01 01:00:00             NaN             NaN             NaN   \n",
       "2010-01-01 02:00:00             NaN             NaN             NaN   \n",
       "2010-01-01 03:00:00             NaN             NaN             NaN   \n",
       "2010-01-01 04:00:00             NaN             NaN             NaN   \n",
       "\n",
       "                     Windspeed_lag9  ...  Windspeed_lag15  Windspeed_lag16  \\\n",
       "datetime                             ...                                     \n",
       "2010-01-01 00:00:00             NaN  ...              NaN              NaN   \n",
       "2010-01-01 01:00:00             NaN  ...              NaN              NaN   \n",
       "2010-01-01 02:00:00             NaN  ...              NaN              NaN   \n",
       "2010-01-01 03:00:00             NaN  ...              NaN              NaN   \n",
       "2010-01-01 04:00:00             NaN  ...              NaN              NaN   \n",
       "\n",
       "                     Windspeed_lag17  Windspeed_lag18  Windspeed_lag19  \\\n",
       "datetime                                                                 \n",
       "2010-01-01 00:00:00              NaN              NaN              NaN   \n",
       "2010-01-01 01:00:00              NaN              NaN              NaN   \n",
       "2010-01-01 02:00:00              NaN              NaN              NaN   \n",
       "2010-01-01 03:00:00              NaN              NaN              NaN   \n",
       "2010-01-01 04:00:00              NaN              NaN              NaN   \n",
       "\n",
       "                     Windspeed_lag20  Windspeed_lag21  Windspeed_lag22  \\\n",
       "datetime                                                                 \n",
       "2010-01-01 00:00:00              NaN              NaN              NaN   \n",
       "2010-01-01 01:00:00              NaN              NaN              NaN   \n",
       "2010-01-01 02:00:00              NaN              NaN              NaN   \n",
       "2010-01-01 03:00:00              NaN              NaN              NaN   \n",
       "2010-01-01 04:00:00              NaN              NaN              NaN   \n",
       "\n",
       "                     Windspeed_lag23     y  \n",
       "datetime                                    \n",
       "2010-01-01 00:00:00              NaN  21.8  \n",
       "2010-01-01 01:00:00              NaN  17.7  \n",
       "2010-01-01 02:00:00              NaN  18.0  \n",
       "2010-01-01 03:00:00              NaN  17.4  \n",
       "2010-01-01 04:00:00              NaN  15.9  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prepare data for ML:\n",
    "\n",
    "df_windspeed_ML = df_windspeed['windspeed'].copy().to_frame() \n",
    "# We want to forecast on horly basis. Let's create windows of 24 Hours we can tune that parameter later\n",
    "forecast_horizon = 1  # next Hour\n",
    "window_size = 24  # use one hour of data\n",
    "for lag in range(window_size-1):\n",
    "    df_windspeed_ML['Windspeed_lag%d' % (lag+1)] = df_windspeed_ML['windspeed'].shift(lag+1)\n",
    "df_windspeed_ML['y'] = df_windspeed_ML['windspeed'].shift(-forecast_horizon)  # our target\n",
    "df_windspeed_ML.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8465daaf-70e7-4d0b-af8a-2c467c188fd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>windspeed</th>\n",
       "      <th>Windspeed_lag1</th>\n",
       "      <th>Windspeed_lag2</th>\n",
       "      <th>Windspeed_lag3</th>\n",
       "      <th>Windspeed_lag4</th>\n",
       "      <th>Windspeed_lag5</th>\n",
       "      <th>Windspeed_lag6</th>\n",
       "      <th>Windspeed_lag7</th>\n",
       "      <th>Windspeed_lag8</th>\n",
       "      <th>Windspeed_lag9</th>\n",
       "      <th>...</th>\n",
       "      <th>Windspeed_lag15</th>\n",
       "      <th>Windspeed_lag16</th>\n",
       "      <th>Windspeed_lag17</th>\n",
       "      <th>Windspeed_lag18</th>\n",
       "      <th>Windspeed_lag19</th>\n",
       "      <th>Windspeed_lag20</th>\n",
       "      <th>Windspeed_lag21</th>\n",
       "      <th>Windspeed_lag22</th>\n",
       "      <th>Windspeed_lag23</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01 23:00:00</th>\n",
       "      <td>4.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>10.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>10.5</td>\n",
       "      <td>10.3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.9</td>\n",
       "      <td>14.9</td>\n",
       "      <td>18.5</td>\n",
       "      <td>...</td>\n",
       "      <td>14.3</td>\n",
       "      <td>12.6</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.9</td>\n",
       "      <td>17.4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>21.8</td>\n",
       "      <td>19.4</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-02 00:00:00</th>\n",
       "      <td>3.3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>10.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>10.5</td>\n",
       "      <td>10.3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.9</td>\n",
       "      <td>14.9</td>\n",
       "      <td>...</td>\n",
       "      <td>14.2</td>\n",
       "      <td>14.3</td>\n",
       "      <td>12.6</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.9</td>\n",
       "      <td>17.4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>21.8</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-02 01:00:00</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>10.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>10.5</td>\n",
       "      <td>10.3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.9</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.2</td>\n",
       "      <td>14.3</td>\n",
       "      <td>12.6</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.9</td>\n",
       "      <td>17.4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-02 02:00:00</th>\n",
       "      <td>4.9</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>10.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>10.5</td>\n",
       "      <td>10.3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>...</td>\n",
       "      <td>18.6</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.2</td>\n",
       "      <td>14.3</td>\n",
       "      <td>12.6</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.9</td>\n",
       "      <td>17.4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-02 03:00:00</th>\n",
       "      <td>4.9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>10.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>10.5</td>\n",
       "      <td>10.3</td>\n",
       "      <td>...</td>\n",
       "      <td>20.1</td>\n",
       "      <td>18.6</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.2</td>\n",
       "      <td>14.3</td>\n",
       "      <td>12.6</td>\n",
       "      <td>15.4</td>\n",
       "      <td>15.9</td>\n",
       "      <td>17.4</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 06:00:00</th>\n",
       "      <td>9.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>9.6</td>\n",
       "      <td>...</td>\n",
       "      <td>11.6</td>\n",
       "      <td>14.8</td>\n",
       "      <td>16.8</td>\n",
       "      <td>18.4</td>\n",
       "      <td>16.4</td>\n",
       "      <td>20.1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 07:00:00</th>\n",
       "      <td>9.6</td>\n",
       "      <td>9.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>...</td>\n",
       "      <td>12.8</td>\n",
       "      <td>11.6</td>\n",
       "      <td>14.8</td>\n",
       "      <td>16.8</td>\n",
       "      <td>18.4</td>\n",
       "      <td>16.4</td>\n",
       "      <td>20.1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 08:00:00</th>\n",
       "      <td>8.9</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.2</td>\n",
       "      <td>12.8</td>\n",
       "      <td>11.6</td>\n",
       "      <td>14.8</td>\n",
       "      <td>16.8</td>\n",
       "      <td>18.4</td>\n",
       "      <td>16.4</td>\n",
       "      <td>20.1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 09:00:00</th>\n",
       "      <td>9.6</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.2</td>\n",
       "      <td>12.8</td>\n",
       "      <td>11.6</td>\n",
       "      <td>14.8</td>\n",
       "      <td>16.8</td>\n",
       "      <td>18.4</td>\n",
       "      <td>16.4</td>\n",
       "      <td>20.1</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 10:00:00</th>\n",
       "      <td>7.8</td>\n",
       "      <td>9.6</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.2</td>\n",
       "      <td>12.8</td>\n",
       "      <td>11.6</td>\n",
       "      <td>14.8</td>\n",
       "      <td>16.8</td>\n",
       "      <td>18.4</td>\n",
       "      <td>16.4</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>122820 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     windspeed  Windspeed_lag1  Windspeed_lag2  \\\n",
       "datetime                                                         \n",
       "2010-01-01 23:00:00        4.8             8.9            10.5   \n",
       "2010-01-02 00:00:00        3.3             4.8             8.9   \n",
       "2010-01-02 01:00:00        5.9             3.3             4.8   \n",
       "2010-01-02 02:00:00        4.9             5.9             3.3   \n",
       "2010-01-02 03:00:00        4.9             4.9             5.9   \n",
       "...                        ...             ...             ...   \n",
       "2024-01-06 06:00:00        9.9             8.4             8.8   \n",
       "2024-01-06 07:00:00        9.6             9.9             8.4   \n",
       "2024-01-06 08:00:00        8.9             9.6             9.9   \n",
       "2024-01-06 09:00:00        9.6             8.9             9.6   \n",
       "2024-01-06 10:00:00        7.8             9.6             8.9   \n",
       "\n",
       "                     Windspeed_lag3  Windspeed_lag4  Windspeed_lag5  \\\n",
       "datetime                                                              \n",
       "2010-01-01 23:00:00             7.6            10.5            10.3   \n",
       "2010-01-02 00:00:00            10.5             7.6            10.5   \n",
       "2010-01-02 01:00:00             8.9            10.5             7.6   \n",
       "2010-01-02 02:00:00             4.8             8.9            10.5   \n",
       "2010-01-02 03:00:00             3.3             4.8             8.9   \n",
       "...                             ...             ...             ...   \n",
       "2024-01-06 06:00:00             8.5             5.6             5.0   \n",
       "2024-01-06 07:00:00             8.8             8.5             5.6   \n",
       "2024-01-06 08:00:00             8.4             8.8             8.5   \n",
       "2024-01-06 09:00:00             9.9             8.4             8.8   \n",
       "2024-01-06 10:00:00             9.6             9.9             8.4   \n",
       "\n",
       "                     Windspeed_lag6  Windspeed_lag7  Windspeed_lag8  \\\n",
       "datetime                                                              \n",
       "2010-01-01 23:00:00             8.5             9.9            14.9   \n",
       "2010-01-02 00:00:00            10.3             8.5             9.9   \n",
       "2010-01-02 01:00:00            10.5            10.3             8.5   \n",
       "2010-01-02 02:00:00             7.6            10.5            10.3   \n",
       "2010-01-02 03:00:00            10.5             7.6            10.5   \n",
       "...                             ...             ...             ...   \n",
       "2024-01-06 06:00:00             7.7             8.0            10.8   \n",
       "2024-01-06 07:00:00             5.0             7.7             8.0   \n",
       "2024-01-06 08:00:00             5.6             5.0             7.7   \n",
       "2024-01-06 09:00:00             8.5             5.6             5.0   \n",
       "2024-01-06 10:00:00             8.8             8.5             5.6   \n",
       "\n",
       "                     Windspeed_lag9  ...  Windspeed_lag15  Windspeed_lag16  \\\n",
       "datetime                             ...                                     \n",
       "2010-01-01 23:00:00            18.5  ...             14.3             12.6   \n",
       "2010-01-02 00:00:00            14.9  ...             14.2             14.3   \n",
       "2010-01-02 01:00:00             9.9  ...             14.0             14.2   \n",
       "2010-01-02 02:00:00             8.5  ...             18.6             14.0   \n",
       "2010-01-02 03:00:00            10.3  ...             20.1             18.6   \n",
       "...                             ...  ...              ...              ...   \n",
       "2024-01-06 06:00:00             9.6  ...             11.6             14.8   \n",
       "2024-01-06 07:00:00            10.8  ...             12.8             11.6   \n",
       "2024-01-06 08:00:00             8.0  ...             14.2             12.8   \n",
       "2024-01-06 09:00:00             7.7  ...              8.0             14.2   \n",
       "2024-01-06 10:00:00             5.0  ...              7.8              8.0   \n",
       "\n",
       "                     Windspeed_lag17  Windspeed_lag18  Windspeed_lag19  \\\n",
       "datetime                                                                 \n",
       "2010-01-01 23:00:00             15.4             15.9             17.4   \n",
       "2010-01-02 00:00:00             12.6             15.4             15.9   \n",
       "2010-01-02 01:00:00             14.3             12.6             15.4   \n",
       "2010-01-02 02:00:00             14.2             14.3             12.6   \n",
       "2010-01-02 03:00:00             14.0             14.2             14.3   \n",
       "...                              ...              ...              ...   \n",
       "2024-01-06 06:00:00             16.8             18.4             16.4   \n",
       "2024-01-06 07:00:00             14.8             16.8             18.4   \n",
       "2024-01-06 08:00:00             11.6             14.8             16.8   \n",
       "2024-01-06 09:00:00             12.8             11.6             14.8   \n",
       "2024-01-06 10:00:00             14.2             12.8             11.6   \n",
       "\n",
       "                     Windspeed_lag20  Windspeed_lag21  Windspeed_lag22  \\\n",
       "datetime                                                                 \n",
       "2010-01-01 23:00:00             18.0             17.7             21.8   \n",
       "2010-01-02 00:00:00             17.4             18.0             17.7   \n",
       "2010-01-02 01:00:00             15.9             17.4             18.0   \n",
       "2010-01-02 02:00:00             15.4             15.9             17.4   \n",
       "2010-01-02 03:00:00             12.6             15.4             15.9   \n",
       "...                              ...              ...              ...   \n",
       "2024-01-06 06:00:00             20.1             14.9             18.0   \n",
       "2024-01-06 07:00:00             16.4             20.1             14.9   \n",
       "2024-01-06 08:00:00             18.4             16.4             20.1   \n",
       "2024-01-06 09:00:00             16.8             18.4             16.4   \n",
       "2024-01-06 10:00:00             14.8             16.8             18.4   \n",
       "\n",
       "                     Windspeed_lag23     y  \n",
       "datetime                                    \n",
       "2010-01-01 23:00:00             19.4   3.3  \n",
       "2010-01-02 00:00:00             21.8   5.9  \n",
       "2010-01-02 01:00:00             17.7   4.9  \n",
       "2010-01-02 02:00:00             18.0   4.9  \n",
       "2010-01-02 03:00:00             17.4   3.9  \n",
       "...                              ...   ...  \n",
       "2024-01-06 06:00:00             17.7   9.6  \n",
       "2024-01-06 07:00:00             18.0   8.9  \n",
       "2024-01-06 08:00:00             14.9   9.6  \n",
       "2024-01-06 09:00:00             20.1   7.8  \n",
       "2024-01-06 10:00:00             16.4  16.0  \n",
       "\n",
       "[122820 rows x 25 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We have missing values in the first rows because of the lags. Let's drop those\n",
    "df_windspeed_ML.dropna(inplace=True)\n",
    "df_windspeed_ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e31f8d6c-92a5-4f89-8bab-c99ccd8b7c14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset using the df_windspeed_ML that we created using the time lags\n",
    "features = list(df_windspeed_ML.columns)[:-1]\n",
    "train = df_windspeed_ML['2010':'2020']  \n",
    "validation = df_windspeed_ML.loc['2021':'2022'] \n",
    "test = df_windspeed_ML.loc['2024']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "694efe5d-ceb9-4b19-8d9b-0c6999085c7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression\n",
      "MAE = 2.54\n",
      "MAPE = 202172805926225.66%\n",
      "R2 = 0.84\n",
      "ElasticNet\n",
      "MAE = 2.55\n",
      "MAPE = 205184009994415.22%\n",
      "R2 = 0.84\n",
      "Ridge\n",
      "MAE = 2.54\n",
      "MAPE = 202172802239404.47%\n",
      "R2 = 0.84\n",
      "Lasso\n",
      "MAE = 2.56\n",
      "MAPE = 206529330788354.19%\n",
      "R2 = 0.83\n",
      "Huber\n",
      "MAE = 2.54\n",
      "MAPE = 193550721087580.88%\n",
      "R2 = 0.83\n"
     ]
    }
   ],
   "source": [
    "# Let's have a look at a family of regressors and pick one of them. We're choosing based on the validation set\n",
    "models = [('Linear Regression', LinearRegression()), ('ElasticNet', ElasticNet()), ('Ridge', Ridge()), ('Lasso', Lasso()), ('Huber', HuberRegressor())]\n",
    "for mname, model in models:\n",
    "    model.fit(train[features], train['y'])\n",
    "    validation['y_pred_%s' % mname] = model.predict(validation[features])\n",
    "    print(mname)\n",
    "    measure_forecast_error(validation['y'], validation['y_pred_%s' % mname])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4f0bc4f-881a-493b-9741-b0321762a903",
   "metadata": {},
   "source": [
    "## Neural Networks (Mltilayer Perceptron)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "607683c1-8bbc-4b6a-8be1-2646bc8c866a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "3013/3013 [==============================] - 8s 2ms/step - loss: 13.7683 - val_loss: 12.7747\n",
      "Epoch 2/50\n",
      "3013/3013 [==============================] - 10s 3ms/step - loss: 12.3838 - val_loss: 12.9761\n",
      "Epoch 3/50\n",
      "3013/3013 [==============================] - 10s 3ms/step - loss: 12.2658 - val_loss: 12.1535\n",
      "Epoch 4/50\n",
      "3013/3013 [==============================] - 10s 3ms/step - loss: 12.1712 - val_loss: 12.1048\n",
      "Epoch 5/50\n",
      "3013/3013 [==============================] - 8s 3ms/step - loss: 12.0913 - val_loss: 12.1290\n",
      "Epoch 6/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 12.0699 - val_loss: 12.1918\n",
      "Epoch 7/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 12.0819 - val_loss: 12.1580\n",
      "Epoch 8/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 12.0302 - val_loss: 12.2081\n",
      "Epoch 9/50\n",
      "3013/3013 [==============================] - 6s 2ms/step - loss: 11.9795 - val_loss: 12.1827\n",
      "Epoch 10/50\n",
      "3013/3013 [==============================] - 6s 2ms/step - loss: 11.9641 - val_loss: 12.1172\n",
      "Epoch 11/50\n",
      "3013/3013 [==============================] - 6s 2ms/step - loss: 11.9488 - val_loss: 12.2515\n",
      "Epoch 12/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.9506 - val_loss: 12.2178\n",
      "Epoch 13/50\n",
      "3013/3013 [==============================] - 6s 2ms/step - loss: 11.9302 - val_loss: 12.0891\n",
      "Epoch 14/50\n",
      "3013/3013 [==============================] - 6s 2ms/step - loss: 11.9426 - val_loss: 12.0584\n",
      "Epoch 15/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.8989 - val_loss: 12.0763\n",
      "Epoch 16/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.9255 - val_loss: 12.2910\n",
      "Epoch 17/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.8920 - val_loss: 12.0839\n",
      "Epoch 18/50\n",
      "3013/3013 [==============================] - 6s 2ms/step - loss: 11.8816 - val_loss: 12.1244\n",
      "Epoch 19/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.8733 - val_loss: 12.2002\n",
      "Epoch 20/50\n",
      "3013/3013 [==============================] - 6s 2ms/step - loss: 11.8728 - val_loss: 12.1380\n",
      "Epoch 21/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.8764 - val_loss: 12.1327\n",
      "Epoch 22/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.8546 - val_loss: 12.0721\n",
      "Epoch 23/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.8722 - val_loss: 12.0907\n",
      "Epoch 24/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.8303 - val_loss: 12.1466\n",
      "Epoch 25/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.8667 - val_loss: 12.2687\n",
      "Epoch 26/50\n",
      "3013/3013 [==============================] - 8s 3ms/step - loss: 11.8521 - val_loss: 12.1597\n",
      "Epoch 27/50\n",
      "3013/3013 [==============================] - 8s 3ms/step - loss: 11.8607 - val_loss: 13.0395\n",
      "Epoch 28/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.8251 - val_loss: 12.3726\n",
      "Epoch 29/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.8254 - val_loss: 12.1806\n",
      "Epoch 30/50\n",
      "3013/3013 [==============================] - 6s 2ms/step - loss: 11.7964 - val_loss: 12.1593\n",
      "Epoch 31/50\n",
      "3013/3013 [==============================] - 6s 2ms/step - loss: 11.8030 - val_loss: 12.2765\n",
      "Epoch 32/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.8178 - val_loss: 12.2897\n",
      "Epoch 33/50\n",
      "3013/3013 [==============================] - 6s 2ms/step - loss: 11.8207 - val_loss: 12.2406\n",
      "Epoch 34/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.8010 - val_loss: 12.0689\n",
      "Epoch 35/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.7689 - val_loss: 12.4807\n",
      "Epoch 36/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.7964 - val_loss: 12.1363\n",
      "Epoch 37/50\n",
      "3013/3013 [==============================] - 6s 2ms/step - loss: 11.7943 - val_loss: 12.1428\n",
      "Epoch 38/50\n",
      "3013/3013 [==============================] - 6s 2ms/step - loss: 11.7981 - val_loss: 12.2510\n",
      "Epoch 39/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.7714 - val_loss: 12.1662\n",
      "Epoch 40/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.7884 - val_loss: 12.6624\n",
      "Epoch 41/50\n",
      "3013/3013 [==============================] - 6s 2ms/step - loss: 11.7738 - val_loss: 12.3266\n",
      "Epoch 42/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.7734 - val_loss: 12.1956\n",
      "Epoch 43/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.7820 - val_loss: 12.1124\n",
      "Epoch 44/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.7596 - val_loss: 12.1440\n",
      "Epoch 45/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.7624 - val_loss: 12.2379\n",
      "Epoch 46/50\n",
      "3013/3013 [==============================] - 6s 2ms/step - loss: 11.7510 - val_loss: 13.1310\n",
      "Epoch 47/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.7536 - val_loss: 12.1031\n",
      "Epoch 48/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.7585 - val_loss: 12.1949\n",
      "Epoch 49/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.7437 - val_loss: 12.0933\n",
      "Epoch 50/50\n",
      "3013/3013 [==============================] - 7s 2ms/step - loss: 11.7493 - val_loss: 12.1461\n",
      "548/548 [==============================] - 1s 2ms/step\n",
      "MAE = 2.54\n",
      "MAPE = 188008945190835.72%\n",
      "R2 = 0.84\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "# Define the model\n",
    "model = Sequential([\n",
    "    Dense(100, activation='relu', input_shape=(len(features),)),\n",
    "    Dense(50, activation='relu'),\n",
    "    Dense(1)  # Output layer with 1 neuron for regression\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer=Adam(), loss='mean_squared_error')\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(train[features], train['y'], validation_data=(validation[features], validation['y']), epochs=50, batch_size=32)\n",
    "\n",
    "# Evaluate the model\n",
    "validation['y_pred_MLP'] = model.predict(validation[features])\n",
    "measure_forecast_error(validation['y'], validation['y_pred_MLP'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "54e837a8-7183-4e52-9577-0f1b97d50170",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "test['y_pred_MLP'] = model.predict(test[features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "41b57515-cbf5-4b5e-904d-73f7d1f29526",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>y</th>\n",
       "      <th>y_pred_MLP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-01-05 00:00:00</th>\n",
       "      <td>28.0</td>\n",
       "      <td>23.755999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 01:00:00</th>\n",
       "      <td>28.7</td>\n",
       "      <td>27.441980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 02:00:00</th>\n",
       "      <td>31.8</td>\n",
       "      <td>28.072094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 03:00:00</th>\n",
       "      <td>22.9</td>\n",
       "      <td>30.981529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 04:00:00</th>\n",
       "      <td>23.0</td>\n",
       "      <td>20.333801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 05:00:00</th>\n",
       "      <td>21.8</td>\n",
       "      <td>21.587736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 06:00:00</th>\n",
       "      <td>17.7</td>\n",
       "      <td>20.048281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 07:00:00</th>\n",
       "      <td>18.0</td>\n",
       "      <td>15.925953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 08:00:00</th>\n",
       "      <td>14.9</td>\n",
       "      <td>16.705627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 09:00:00</th>\n",
       "      <td>20.1</td>\n",
       "      <td>14.124292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 10:00:00</th>\n",
       "      <td>16.4</td>\n",
       "      <td>18.714577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 11:00:00</th>\n",
       "      <td>18.4</td>\n",
       "      <td>16.387684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 12:00:00</th>\n",
       "      <td>16.8</td>\n",
       "      <td>18.092815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 13:00:00</th>\n",
       "      <td>14.8</td>\n",
       "      <td>17.173496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 14:00:00</th>\n",
       "      <td>11.6</td>\n",
       "      <td>15.370811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 15:00:00</th>\n",
       "      <td>12.8</td>\n",
       "      <td>12.055673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 16:00:00</th>\n",
       "      <td>14.2</td>\n",
       "      <td>11.903522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 17:00:00</th>\n",
       "      <td>8.0</td>\n",
       "      <td>12.787566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 18:00:00</th>\n",
       "      <td>7.8</td>\n",
       "      <td>8.912105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 19:00:00</th>\n",
       "      <td>12.9</td>\n",
       "      <td>8.264073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 20:00:00</th>\n",
       "      <td>9.6</td>\n",
       "      <td>12.509823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 21:00:00</th>\n",
       "      <td>10.8</td>\n",
       "      <td>10.542394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 22:00:00</th>\n",
       "      <td>8.0</td>\n",
       "      <td>11.420262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 23:00:00</th>\n",
       "      <td>7.7</td>\n",
       "      <td>9.459938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 00:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8.911482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 01:00:00</th>\n",
       "      <td>5.6</td>\n",
       "      <td>6.897420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 02:00:00</th>\n",
       "      <td>8.5</td>\n",
       "      <td>6.842935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 03:00:00</th>\n",
       "      <td>8.8</td>\n",
       "      <td>8.720316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 04:00:00</th>\n",
       "      <td>8.4</td>\n",
       "      <td>9.212708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 05:00:00</th>\n",
       "      <td>9.9</td>\n",
       "      <td>9.030958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 06:00:00</th>\n",
       "      <td>9.6</td>\n",
       "      <td>10.087243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 07:00:00</th>\n",
       "      <td>8.9</td>\n",
       "      <td>9.960232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 08:00:00</th>\n",
       "      <td>9.6</td>\n",
       "      <td>9.729853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 09:00:00</th>\n",
       "      <td>7.8</td>\n",
       "      <td>9.767982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 10:00:00</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8.829248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        y  y_pred_MLP\n",
       "datetime                             \n",
       "2024-01-05 00:00:00  28.0   23.755999\n",
       "2024-01-05 01:00:00  28.7   27.441980\n",
       "2024-01-05 02:00:00  31.8   28.072094\n",
       "2024-01-05 03:00:00  22.9   30.981529\n",
       "2024-01-05 04:00:00  23.0   20.333801\n",
       "2024-01-05 05:00:00  21.8   21.587736\n",
       "2024-01-05 06:00:00  17.7   20.048281\n",
       "2024-01-05 07:00:00  18.0   15.925953\n",
       "2024-01-05 08:00:00  14.9   16.705627\n",
       "2024-01-05 09:00:00  20.1   14.124292\n",
       "2024-01-05 10:00:00  16.4   18.714577\n",
       "2024-01-05 11:00:00  18.4   16.387684\n",
       "2024-01-05 12:00:00  16.8   18.092815\n",
       "2024-01-05 13:00:00  14.8   17.173496\n",
       "2024-01-05 14:00:00  11.6   15.370811\n",
       "2024-01-05 15:00:00  12.8   12.055673\n",
       "2024-01-05 16:00:00  14.2   11.903522\n",
       "2024-01-05 17:00:00   8.0   12.787566\n",
       "2024-01-05 18:00:00   7.8    8.912105\n",
       "2024-01-05 19:00:00  12.9    8.264073\n",
       "2024-01-05 20:00:00   9.6   12.509823\n",
       "2024-01-05 21:00:00  10.8   10.542394\n",
       "2024-01-05 22:00:00   8.0   11.420262\n",
       "2024-01-05 23:00:00   7.7    9.459938\n",
       "2024-01-06 00:00:00   5.0    8.911482\n",
       "2024-01-06 01:00:00   5.6    6.897420\n",
       "2024-01-06 02:00:00   8.5    6.842935\n",
       "2024-01-06 03:00:00   8.8    8.720316\n",
       "2024-01-06 04:00:00   8.4    9.212708\n",
       "2024-01-06 05:00:00   9.9    9.030958\n",
       "2024-01-06 06:00:00   9.6   10.087243\n",
       "2024-01-06 07:00:00   8.9    9.960232\n",
       "2024-01-06 08:00:00   9.6    9.729853\n",
       "2024-01-06 09:00:00   7.8    9.767982\n",
       "2024-01-06 10:00:00  16.0    8.829248"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_powercal_24H = test.loc[\"2024-01-05\":\"2024-01-06\", test.columns[-2:]] #Last 24 Hours of data for power cal (Actual vs Pred pwer)\n",
    "df_powercal_24H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d1950c15-c67b-4b82-b5ff-19b9bea1ed11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>y_pred_MLP</th>\n",
       "      <th>power_actual(KW)</th>\n",
       "      <th>power_pridicted(KW)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-01-05 00:00:00</th>\n",
       "      <td>28.0</td>\n",
       "      <td>23.755999</td>\n",
       "      <td>827915.0</td>\n",
       "      <td>505628.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 01:00:00</th>\n",
       "      <td>28.7</td>\n",
       "      <td>27.441980</td>\n",
       "      <td>891574.0</td>\n",
       "      <td>779396.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 02:00:00</th>\n",
       "      <td>31.8</td>\n",
       "      <td>28.072094</td>\n",
       "      <td>1212811.0</td>\n",
       "      <td>834327.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 03:00:00</th>\n",
       "      <td>22.9</td>\n",
       "      <td>30.981529</td>\n",
       "      <td>452916.0</td>\n",
       "      <td>1121555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 04:00:00</th>\n",
       "      <td>23.0</td>\n",
       "      <td>20.333801</td>\n",
       "      <td>458876.0</td>\n",
       "      <td>317079.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 05:00:00</th>\n",
       "      <td>21.8</td>\n",
       "      <td>21.587736</td>\n",
       "      <td>390734.0</td>\n",
       "      <td>379431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 06:00:00</th>\n",
       "      <td>17.7</td>\n",
       "      <td>20.048281</td>\n",
       "      <td>209137.0</td>\n",
       "      <td>303908.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 07:00:00</th>\n",
       "      <td>18.0</td>\n",
       "      <td>15.925953</td>\n",
       "      <td>219952.0</td>\n",
       "      <td>152345.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 08:00:00</th>\n",
       "      <td>14.9</td>\n",
       "      <td>16.705627</td>\n",
       "      <td>124758.0</td>\n",
       "      <td>175833.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 09:00:00</th>\n",
       "      <td>20.1</td>\n",
       "      <td>14.124292</td>\n",
       "      <td>306267.0</td>\n",
       "      <td>106270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 10:00:00</th>\n",
       "      <td>16.4</td>\n",
       "      <td>18.714577</td>\n",
       "      <td>166357.0</td>\n",
       "      <td>247202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 11:00:00</th>\n",
       "      <td>18.4</td>\n",
       "      <td>16.387684</td>\n",
       "      <td>234944.0</td>\n",
       "      <td>165983.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 12:00:00</th>\n",
       "      <td>16.8</td>\n",
       "      <td>18.092815</td>\n",
       "      <td>178829.0</td>\n",
       "      <td>223372.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 13:00:00</th>\n",
       "      <td>14.8</td>\n",
       "      <td>17.173496</td>\n",
       "      <td>122263.0</td>\n",
       "      <td>191024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 14:00:00</th>\n",
       "      <td>11.6</td>\n",
       "      <td>15.370811</td>\n",
       "      <td>58868.0</td>\n",
       "      <td>136962.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 15:00:00</th>\n",
       "      <td>12.8</td>\n",
       "      <td>12.055673</td>\n",
       "      <td>79093.0</td>\n",
       "      <td>66082.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 16:00:00</th>\n",
       "      <td>14.2</td>\n",
       "      <td>11.903522</td>\n",
       "      <td>107988.0</td>\n",
       "      <td>63611.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 17:00:00</th>\n",
       "      <td>8.0</td>\n",
       "      <td>12.787566</td>\n",
       "      <td>19309.0</td>\n",
       "      <td>78863.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 18:00:00</th>\n",
       "      <td>7.8</td>\n",
       "      <td>8.912105</td>\n",
       "      <td>17897.0</td>\n",
       "      <td>26696.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 19:00:00</th>\n",
       "      <td>12.9</td>\n",
       "      <td>8.264073</td>\n",
       "      <td>80961.0</td>\n",
       "      <td>21286.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 20:00:00</th>\n",
       "      <td>9.6</td>\n",
       "      <td>12.509823</td>\n",
       "      <td>33367.0</td>\n",
       "      <td>73835.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 21:00:00</th>\n",
       "      <td>10.8</td>\n",
       "      <td>10.542394</td>\n",
       "      <td>47509.0</td>\n",
       "      <td>44190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 22:00:00</th>\n",
       "      <td>8.0</td>\n",
       "      <td>11.420262</td>\n",
       "      <td>19309.0</td>\n",
       "      <td>56174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05 23:00:00</th>\n",
       "      <td>7.7</td>\n",
       "      <td>9.459938</td>\n",
       "      <td>17218.0</td>\n",
       "      <td>31928.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 00:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8.911482</td>\n",
       "      <td>4714.0</td>\n",
       "      <td>26690.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 01:00:00</th>\n",
       "      <td>5.6</td>\n",
       "      <td>6.897420</td>\n",
       "      <td>6623.0</td>\n",
       "      <td>12375.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 02:00:00</th>\n",
       "      <td>8.5</td>\n",
       "      <td>6.842935</td>\n",
       "      <td>23161.0</td>\n",
       "      <td>12084.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 03:00:00</th>\n",
       "      <td>8.8</td>\n",
       "      <td>8.720316</td>\n",
       "      <td>25701.0</td>\n",
       "      <td>25009.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 04:00:00</th>\n",
       "      <td>8.4</td>\n",
       "      <td>9.212708</td>\n",
       "      <td>22353.0</td>\n",
       "      <td>29489.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 05:00:00</th>\n",
       "      <td>9.9</td>\n",
       "      <td>9.030958</td>\n",
       "      <td>36594.0</td>\n",
       "      <td>27778.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 06:00:00</th>\n",
       "      <td>9.6</td>\n",
       "      <td>10.087243</td>\n",
       "      <td>33367.0</td>\n",
       "      <td>38710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 07:00:00</th>\n",
       "      <td>8.9</td>\n",
       "      <td>9.960232</td>\n",
       "      <td>26587.0</td>\n",
       "      <td>37266.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 08:00:00</th>\n",
       "      <td>9.6</td>\n",
       "      <td>9.729853</td>\n",
       "      <td>33367.0</td>\n",
       "      <td>34740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 09:00:00</th>\n",
       "      <td>7.8</td>\n",
       "      <td>9.767982</td>\n",
       "      <td>17897.0</td>\n",
       "      <td>35150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06 10:00:00</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8.829248</td>\n",
       "      <td>154479.0</td>\n",
       "      <td>25958.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        y  y_pred_MLP  power_actual(KW)  power_pridicted(KW)\n",
       "datetime                                                                    \n",
       "2024-01-05 00:00:00  28.0   23.755999          827915.0             505628.0\n",
       "2024-01-05 01:00:00  28.7   27.441980          891574.0             779396.0\n",
       "2024-01-05 02:00:00  31.8   28.072094         1212811.0             834327.0\n",
       "2024-01-05 03:00:00  22.9   30.981529          452916.0            1121555.0\n",
       "2024-01-05 04:00:00  23.0   20.333801          458876.0             317079.0\n",
       "2024-01-05 05:00:00  21.8   21.587736          390734.0             379431.0\n",
       "2024-01-05 06:00:00  17.7   20.048281          209137.0             303908.0\n",
       "2024-01-05 07:00:00  18.0   15.925953          219952.0             152345.0\n",
       "2024-01-05 08:00:00  14.9   16.705627          124758.0             175833.0\n",
       "2024-01-05 09:00:00  20.1   14.124292          306267.0             106270.0\n",
       "2024-01-05 10:00:00  16.4   18.714577          166357.0             247202.0\n",
       "2024-01-05 11:00:00  18.4   16.387684          234944.0             165983.0\n",
       "2024-01-05 12:00:00  16.8   18.092815          178829.0             223372.0\n",
       "2024-01-05 13:00:00  14.8   17.173496          122263.0             191024.0\n",
       "2024-01-05 14:00:00  11.6   15.370811           58868.0             136962.0\n",
       "2024-01-05 15:00:00  12.8   12.055673           79093.0              66082.0\n",
       "2024-01-05 16:00:00  14.2   11.903522          107988.0              63611.0\n",
       "2024-01-05 17:00:00   8.0   12.787566           19309.0              78863.0\n",
       "2024-01-05 18:00:00   7.8    8.912105           17897.0              26696.0\n",
       "2024-01-05 19:00:00  12.9    8.264073           80961.0              21286.0\n",
       "2024-01-05 20:00:00   9.6   12.509823           33367.0              73835.0\n",
       "2024-01-05 21:00:00  10.8   10.542394           47509.0              44190.0\n",
       "2024-01-05 22:00:00   8.0   11.420262           19309.0              56174.0\n",
       "2024-01-05 23:00:00   7.7    9.459938           17218.0              31928.0\n",
       "2024-01-06 00:00:00   5.0    8.911482            4714.0              26690.0\n",
       "2024-01-06 01:00:00   5.6    6.897420            6623.0              12375.0\n",
       "2024-01-06 02:00:00   8.5    6.842935           23161.0              12084.0\n",
       "2024-01-06 03:00:00   8.8    8.720316           25701.0              25009.0\n",
       "2024-01-06 04:00:00   8.4    9.212708           22353.0              29489.0\n",
       "2024-01-06 05:00:00   9.9    9.030958           36594.0              27778.0\n",
       "2024-01-06 06:00:00   9.6   10.087243           33367.0              38710.0\n",
       "2024-01-06 07:00:00   8.9    9.960232           26587.0              37266.0\n",
       "2024-01-06 08:00:00   9.6    9.729853           33367.0              34740.0\n",
       "2024-01-06 09:00:00   7.8    9.767982           17897.0              35150.0\n",
       "2024-01-06 10:00:00  16.0    8.829248          154479.0              25958.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Define the given values\n",
    "density = 1.225  # density in kg/m^3\n",
    "radius = 140  # radius in meters\n",
    "area = np.pi * (radius ** 2)  # area in square meters\n",
    "\n",
    "# actual and pridicted wind speed (velocity) from the first column of the DataFrame \n",
    "actual_velocity = df_powercal_24H.iloc[:, 0]  \n",
    "pridicted_velocity= df_powercal_24H.iloc[:, 1]\n",
    "\n",
    "# Calculate power using the formula\n",
    "actual_power = 0.5 * density * area * (actual_velocity ** 3)//1000\n",
    "pridicted_power= 0.5 * density * area * (pridicted_velocity ** 3)//1000\n",
    "\n",
    "# Add the calculated power as a new column to the DataFrame\n",
    "df_powercal_24H['power_actual(KW)'] = actual_power \n",
    "df_powercal_24H['power_pridicted(KW)'] = pridicted_power\n",
    "\n",
    "df_powercal_24H"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c75348d3-c71f-426a-911d-377d042b1289",
   "metadata": {},
   "source": [
    "## Pridicting the next 24 Hours windspeed for Power calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cca6a255-f160-4ffb-9b95-fca4f70326c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>daily_windspeed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>14.287500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-02</th>\n",
       "      <td>10.758333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-03</th>\n",
       "      <td>10.870833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>11.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>7.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-02</th>\n",
       "      <td>28.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-03</th>\n",
       "      <td>21.845833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-04</th>\n",
       "      <td>15.537500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-05</th>\n",
       "      <td>17.262500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-06</th>\n",
       "      <td>8.816667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5119 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            daily_windspeed\n",
       "datetime                   \n",
       "2010-01-01        14.287500\n",
       "2010-01-02        10.758333\n",
       "2010-01-03        10.870833\n",
       "2010-01-04        11.875000\n",
       "2010-01-05         7.900000\n",
       "...                     ...\n",
       "2024-01-02        28.966667\n",
       "2024-01-03        21.845833\n",
       "2024-01-04        15.537500\n",
       "2024-01-05        17.262500\n",
       "2024-01-06         8.816667\n",
       "\n",
       "[5119 rows x 1 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert the hourly data to daily so that we can pridicit the windspeed for next day. If the data in hourly we will be able to predicit the next hour\n",
    "# and use that predicition to make new features (by time lag) for another next hour and then do prediction however the pridiction loss \n",
    "#is too much for horly data\n",
    "df_windspeed_daily = df_windspeed['windspeed'].copy().to_frame() \n",
    "df_windspeed_daily[['daily_windspeed']] = df_windspeed_daily.resample('D').mean().ffill()\n",
    "df_windspeed_daily.dropna(inplace=True)\n",
    "df_windspeed_daily.drop(columns=['windspeed'], inplace=True)\n",
    "df_windspeed_daily\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0f0d77e1-884f-4ce9-9ccb-0197de13017e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>daily_windspeed</th>\n",
       "      <th>daily_Windspeed_lag1</th>\n",
       "      <th>daily_Windspeed_lag2</th>\n",
       "      <th>daily_Windspeed_lag3</th>\n",
       "      <th>daily_Windspeed_lag4</th>\n",
       "      <th>daily_Windspeed_lag5</th>\n",
       "      <th>daily_Windspeed_lag6</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>17.879167</td>\n",
       "      <td>21.883333</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>11.875000</td>\n",
       "      <td>10.870833</td>\n",
       "      <td>10.758333</td>\n",
       "      <td>14.287500</td>\n",
       "      <td>16.058333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>16.058333</td>\n",
       "      <td>17.879167</td>\n",
       "      <td>21.883333</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>11.875000</td>\n",
       "      <td>10.870833</td>\n",
       "      <td>10.758333</td>\n",
       "      <td>24.541667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-09</th>\n",
       "      <td>24.541667</td>\n",
       "      <td>16.058333</td>\n",
       "      <td>17.879167</td>\n",
       "      <td>21.883333</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>11.875000</td>\n",
       "      <td>10.870833</td>\n",
       "      <td>15.658333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-10</th>\n",
       "      <td>15.658333</td>\n",
       "      <td>24.541667</td>\n",
       "      <td>16.058333</td>\n",
       "      <td>17.879167</td>\n",
       "      <td>21.883333</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>11.875000</td>\n",
       "      <td>9.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>9.187500</td>\n",
       "      <td>15.658333</td>\n",
       "      <td>24.541667</td>\n",
       "      <td>16.058333</td>\n",
       "      <td>17.879167</td>\n",
       "      <td>21.883333</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>18.829167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            daily_windspeed  daily_Windspeed_lag1  daily_Windspeed_lag2  \\\n",
       "datetime                                                                  \n",
       "2010-01-07        17.879167             21.883333              7.900000   \n",
       "2010-01-08        16.058333             17.879167             21.883333   \n",
       "2010-01-09        24.541667             16.058333             17.879167   \n",
       "2010-01-10        15.658333             24.541667             16.058333   \n",
       "2010-01-11         9.187500             15.658333             24.541667   \n",
       "\n",
       "            daily_Windspeed_lag3  daily_Windspeed_lag4  daily_Windspeed_lag5  \\\n",
       "datetime                                                                       \n",
       "2010-01-07             11.875000             10.870833             10.758333   \n",
       "2010-01-08              7.900000             11.875000             10.870833   \n",
       "2010-01-09             21.883333              7.900000             11.875000   \n",
       "2010-01-10             17.879167             21.883333              7.900000   \n",
       "2010-01-11             16.058333             17.879167             21.883333   \n",
       "\n",
       "            daily_Windspeed_lag6          y  \n",
       "datetime                                     \n",
       "2010-01-07             14.287500  16.058333  \n",
       "2010-01-08             10.758333  24.541667  \n",
       "2010-01-09             10.870833  15.658333  \n",
       "2010-01-10             11.875000   9.187500  \n",
       "2010-01-11              7.900000  18.829167  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_windspeed_daily_ML = df_windspeed_daily['daily_windspeed'].copy().to_frame() \n",
    "# We want to forecast on horly basis. Let's create windows of 24 Hours we can tune that parameter later\n",
    "forecast_horizon = 1  # next day\n",
    "window_size = 7  # use past 7 days of data\n",
    "for lag in range(window_size-1):\n",
    "    df_windspeed_daily_ML['daily_Windspeed_lag%d' % (lag+1)] = df_windspeed_daily_ML['daily_windspeed'].shift(lag+1)\n",
    "df_windspeed_daily_ML['y'] = df_windspeed_daily_ML['daily_windspeed'].shift(-forecast_horizon)  # our target\n",
    "df_windspeed_daily_ML.dropna(inplace=True)\n",
    "df_windspeed_daily_ML.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f140c38c-351d-4bfc-8a7b-f39bb061dde8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset using the df_windspeed_ML that we created using the time lags\n",
    "features_daily = list(df_windspeed_daily_ML.columns)[:-1]\n",
    "train_daily = df_windspeed_daily_ML['2010':'2020']  \n",
    "validation_daily = df_windspeed_daily_ML.loc['2021':'2022'] \n",
    "test_daily = df_windspeed_daily_ML.loc['2024']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b78707ad-e030-4a9b-a29f-9b0f7bcfe8b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "126/126 [==============================] - 1s 4ms/step - loss: 89.2633 - val_loss: 33.3986\n",
      "Epoch 2/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 37.3680 - val_loss: 32.3371\n",
      "Epoch 3/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 36.6043 - val_loss: 32.0348\n",
      "Epoch 4/50\n",
      "126/126 [==============================] - 1s 4ms/step - loss: 36.4481 - val_loss: 32.0236\n",
      "Epoch 5/50\n",
      "126/126 [==============================] - 0s 3ms/step - loss: 36.0811 - val_loss: 31.7711\n",
      "Epoch 6/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 36.2937 - val_loss: 32.9963\n",
      "Epoch 7/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 35.8600 - val_loss: 32.9330\n",
      "Epoch 8/50\n",
      "126/126 [==============================] - 0s 3ms/step - loss: 35.5251 - val_loss: 31.4134\n",
      "Epoch 9/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 35.3245 - val_loss: 31.6714\n",
      "Epoch 10/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 35.3452 - val_loss: 32.4805\n",
      "Epoch 11/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 34.8360 - val_loss: 31.0188\n",
      "Epoch 12/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 34.8466 - val_loss: 31.3238\n",
      "Epoch 13/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 34.8065 - val_loss: 31.8549\n",
      "Epoch 14/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 34.5962 - val_loss: 30.7885\n",
      "Epoch 15/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 34.5758 - val_loss: 30.6124\n",
      "Epoch 16/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 34.6718 - val_loss: 30.8206\n",
      "Epoch 17/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 34.1958 - val_loss: 32.9759\n",
      "Epoch 18/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 34.4401 - val_loss: 30.2346\n",
      "Epoch 19/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 33.9249 - val_loss: 30.4191\n",
      "Epoch 20/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 34.0404 - val_loss: 30.4372\n",
      "Epoch 21/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 34.0943 - val_loss: 30.6068\n",
      "Epoch 22/50\n",
      "126/126 [==============================] - 0s 3ms/step - loss: 34.2301 - val_loss: 30.7185\n",
      "Epoch 23/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 34.0305 - val_loss: 30.7353\n",
      "Epoch 24/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 33.7234 - val_loss: 30.4155\n",
      "Epoch 25/50\n",
      "126/126 [==============================] - 0s 3ms/step - loss: 33.7015 - val_loss: 30.4174\n",
      "Epoch 26/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 34.5942 - val_loss: 31.7424\n",
      "Epoch 27/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 33.7475 - val_loss: 30.5391\n",
      "Epoch 28/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 33.5124 - val_loss: 30.3652\n",
      "Epoch 29/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 33.8622 - val_loss: 31.0799\n",
      "Epoch 30/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 33.4343 - val_loss: 31.0704\n",
      "Epoch 31/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 33.8005 - val_loss: 30.4095\n",
      "Epoch 32/50\n",
      "126/126 [==============================] - 0s 3ms/step - loss: 33.6338 - val_loss: 30.5437\n",
      "Epoch 33/50\n",
      "126/126 [==============================] - 0s 3ms/step - loss: 33.3438 - val_loss: 31.8773\n",
      "Epoch 34/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 33.5423 - val_loss: 31.3860\n",
      "Epoch 35/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 33.5009 - val_loss: 31.5730\n",
      "Epoch 36/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 33.3427 - val_loss: 30.3678\n",
      "Epoch 37/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 33.4121 - val_loss: 30.8836\n",
      "Epoch 38/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 33.0856 - val_loss: 31.1704\n",
      "Epoch 39/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 33.1263 - val_loss: 30.8077\n",
      "Epoch 40/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 32.9436 - val_loss: 31.3661\n",
      "Epoch 41/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 32.7574 - val_loss: 30.4177\n",
      "Epoch 42/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 33.3421 - val_loss: 32.1277\n",
      "Epoch 43/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 33.0497 - val_loss: 30.6984\n",
      "Epoch 44/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 32.9442 - val_loss: 31.8784\n",
      "Epoch 45/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 33.2163 - val_loss: 30.8121\n",
      "Epoch 46/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 32.7426 - val_loss: 30.9961\n",
      "Epoch 47/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 32.9287 - val_loss: 31.3918\n",
      "Epoch 48/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 32.9854 - val_loss: 30.3787\n",
      "Epoch 49/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 32.8679 - val_loss: 30.9297\n",
      "Epoch 50/50\n",
      "126/126 [==============================] - 0s 2ms/step - loss: 32.6109 - val_loss: 30.6830\n",
      "23/23 [==============================] - 0s 1ms/step\n",
      "MAE = 4.21\n",
      "MAPE = 30.79%\n",
      "R2 = 0.26\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "# Define the model\n",
    "model_daily = Sequential([\n",
    "    Dense(100, activation='relu', input_shape=(len(features_daily),)),\n",
    "    Dense(50, activation='relu'),\n",
    "    Dense(1)  # Output layer with 1 neuron for regression\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model_daily.compile(optimizer=Adam(), loss='mean_squared_error')\n",
    "\n",
    "# # Train the model\n",
    "history_daily = model_daily.fit(train_daily[features_daily], train_daily['y'], validation_data=(validation_daily[features_daily], validation_daily['y']), epochs=50, batch_size=32)\n",
    "\n",
    "\n",
    "# Evaluate the model\n",
    "validation_daily['y_pred_MLP'] = model_daily.predict(validation_daily[features_daily])\n",
    "measure_forecast_error(validation_daily['y'], validation_daily['y_pred_MLP'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "535648af-75f1-4cd5-a263-a9bfd5396ba9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 27ms/step\n"
     ]
    }
   ],
   "source": [
    "#As discussed on the zoom call it was advised that not to convert the data to Month or daily formate therefore i am prediciting the next hour however \n",
    "#we can scale down the time series to days and then delay the Target \"y\" to next day pridiction.\n",
    "test_daily['y_pred_MLP'] = model_daily.predict(test_daily[features_daily])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "196d07ef-8109-4a6d-bc86-cfc7401e2a3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>y_pred_MLP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-01-05</th>\n",
       "      <td>8.816667</td>\n",
       "      <td>16.365047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                   y  y_pred_MLP\n",
       "datetime                        \n",
       "2024-01-05  8.816667   16.365047"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_powercaldaily_24H= test_daily.loc[[\"2024-01-05\"], test_daily.columns[-2:]] #Last 24 Hours of data for power cal (Actual vs Pred pwer)\n",
    "df_powercaldaily_24H"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b08a6b3-0500-41e5-abd6-42753536a948",
   "metadata": {},
   "source": [
    "P = 0.5 * ρ * A * v^3 where P is the power in watts, ρ is the air density in kilograms per cubic meter, A is the swept area of the rotor in square meters, and v is the wind speed in meters per second. We can find the power generated given we have the rotor length and the density of air in Brighton area. ρ= 1.2041 kg/m3 let the area swept by the rotor as pi*r2= let the blade lenth be 40m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "23b48e17-162e-44cc-973c-e2bc67776ded",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>y</th>\n",
       "      <th>y_pred_MLP</th>\n",
       "      <th>power_actual(KW)</th>\n",
       "      <th>power_pridicted(KW)</th>\n",
       "      <th>power_pridicted(KW/h)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
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       "      <th>2024-01-05</th>\n",
       "      <td>8.816667</td>\n",
       "      <td>16.365047</td>\n",
       "      <td>25847.0</td>\n",
       "      <td>165296.0</td>\n",
       "      <td>165296.0</td>\n",
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      "text/plain": [
       "                   y  y_pred_MLP  power_actual(KW)  power_pridicted(KW)  \\\n",
       "datetime                                                                  \n",
       "2024-01-05  8.816667   16.365047           25847.0             165296.0   \n",
       "\n",
       "            power_pridicted(KW/h)  \n",
       "datetime                           \n",
       "2024-01-05               165296.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Define the given values\n",
    "density = 1.225  # density in kg/m^3\n",
    "radius = 140  # radius in meters\n",
    "area = np.pi * (radius ** 2)  # area in square meters\n",
    "\n",
    "# actual and pridicted wind speed (velocity) from the first column of the DataFrame \n",
    "actual_daily_velocity = df_powercaldaily_24H.iloc[:, 0]  \n",
    "pridicted_daily_velocity= df_powercaldaily_24H.iloc[:, 1]\n",
    "\n",
    "# Calculate power using the formula\n",
    "actual_daily_power = 0.5 * density * area * (actual_daily_velocity ** 3)//1000\n",
    "pridicted_daily_power= 0.5 * density * area * (pridicted_daily_velocity ** 3)//1000\n",
    "\n",
    "# Add the calculated power as a new column to the DataFrame\n",
    "df_powercaldaily_24H['power_actual(KW)'] = actual_daily_power\n",
    "df_powercaldaily_24H['power_pridicted(KW/h)'] = pridicted_daily_power\n",
    "\n",
    "df_powercaldaily_24H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ca221319-521c-4e42-b338-5116ed80c4d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since we delayed the target by -1 the predicted power is the power for the date 6th January. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5007e7f2-4868-453b-a0c7-fdf9c1671bfb",
   "metadata": {},
   "source": [
    "In Brighton and Hove, 384 GWh of electricity was used by residents to power their homes.[1]\n",
    "\n",
    "\n",
    "[1] https://www.sussexexpress.co.uk/your-sussex/east-sussex/brighton-and-hove/record-fall-in-domestic-gas-consumption-in-brighton-and-hove-as-energy-prices-and-cost-of-living-soar-4496103\n",
    "\n",
    "We have 1.6 GWH of electricity produced by a single wind turbine for 6 Jan, 2024. This is power produced in 24 hour. We assume we have 500 wind turbines all over Brighton. so total power produced is 300*1.6=  480GWH therefore we can give free excessive energy to whoever signup  till the quota is full"
   ]
  }
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