6. Improved Deep Mixture Density Network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Improved Deep Mixture Density Network for Regional Wind Power Probabilistic Forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import all the needed packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic Data Science Packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#TensorFlow packages for building DRNN layer\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.layers import SimpleRNN, Dense, Input, Reshape, LSTM, BatchNormalization, Layer\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras import backend as K\n",
    "\n",
    "#Plotting graph\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#Normalization of data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "#Plotting Keras Model\n",
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "#Utility Module for computing and displaying metrics\n",
    "from utility_functions import metrics\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the data and split into Train, Test and Validation\n",
    "- Fetch the first 70% records as train and the rest 15% each as validation and test\n",
    "    - 8711 * 70% = 6097 records from the start (Train)\n",
    "    - 6097 + 1307 = 6098 to 7404 records as (Validation)\n",
    "    - 7404 + 1307 = 7405 to 8711 records as (Test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "thesis_dataset = pd.read_csv('data/final_thesis_dataset.csv',parse_dates=[0], index_col=0)\n",
    "\n",
    "#Extracting dataset into train, validation and test sets\n",
    "train = thesis_dataset[:6097]\n",
    "valid = thesis_dataset[6097:7404]\n",
    "test = thesis_dataset[7404:]\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "#Extracting solar and wind X, y columns per set\n",
    "X_solar_train = scaler.fit_transform(train[['SWTDN', 'SWGDN', 'T']])\n",
    "y_solar_train = scaler.fit_transform(train['DE_solar_generation_actual'].values.reshape(-1,1))\n",
    "X_solar_valid = scaler.fit_transform(valid[['SWTDN', 'SWGDN', 'T']])\n",
    "y_solar_valid = scaler.fit_transform(valid['DE_solar_generation_actual'].values.reshape(-1,1))\n",
    "X_solar_test = scaler.fit_transform(test[['SWTDN', 'SWGDN', 'T']])\n",
    "y_solar_test = scaler.fit_transform(test['DE_solar_generation_actual'].values.reshape(-1,1))\n",
    "\n",
    "X_wind_train = scaler.fit_transform(train[['v1', 'v2', 'v_50m', 'z0']])\n",
    "y_wind_train = scaler.fit_transform(train['DE_wind_generation_actual'].values.reshape(-1,1))\n",
    "X_wind_valid = scaler.fit_transform(valid[['v1', 'v2', 'v_50m', 'z0']])\n",
    "y_wind_valid = scaler.fit_transform(valid['DE_wind_generation_actual'].values.reshape(-1,1))\n",
    "X_wind_test = scaler.fit_transform(test[['v1', 'v2', 'v_50m', 'z0']])\n",
    "y_wind_test = scaler.fit_transform(test['DE_wind_generation_actual'].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a Mixture Density Layer from scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MixtureDensity(Layer):\n",
    "    #Defines custom layer attributes, and state variables that do not depend on input shapes.\n",
    "    def __init__(self, kernelDim, numComponents, **kwargs):\n",
    "        self.hiddenDim = 24\n",
    "        self.kernelDim = kernelDim\n",
    "        self.numComponents = numComponents\n",
    "        self.outputDim = 1\n",
    "        super(MixtureDensity, self).__init__(**kwargs)\n",
    "\n",
    "    #This method can be used to create weights that depend on the shape of the input\n",
    "    #__call__() inside keras will automatically build the layer during invocation by calling this method.\n",
    "    def build(self, inputShape):\n",
    "        self.inputDim = inputShape[1]\n",
    "        self.Wh = K.variable(np.random.normal(scale=0.5,size=(self.inputDim, self.hiddenDim)))\n",
    "        self.bh = K.variable(np.random.normal(scale=0.5,size=(self.hiddenDim)))\n",
    "        self.Wo = K.variable(np.random.normal(scale=0.5,size=(self.hiddenDim, self.outputDim)))\n",
    "        self.bo = K.variable(np.random.normal(scale=0.5,size=(self.outputDim)))\n",
    "\n",
    "        self._trainable_weights = [self.Wh,self.bh,self.Wo,self.bo]\n",
    "\n",
    "    #this performs the logic of applying the layer to the input tensors\n",
    "    def call(self, x, mask=None):\n",
    "        hidden = K.tanh(K.dot(x, self.Wh) + self.bh)\n",
    "        output = K.dot(hidden,self.Wo) + self.bo\n",
    "        return output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building a IDMDN using Deep Layer of BatchNorm and Density for Mixture Density."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "batch_normalization (BatchNo (None, 3)                 12        \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "mixture_density (MixtureDens (None, 1)                 3121      \n",
      "=================================================================\n",
      "Total params: 38,205\n",
      "Trainable params: 37,431\n",
      "Non-trainable params: 774\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "solar_model = Sequential()\n",
    "solar_model.add(BatchNormalization(input_shape=(3,)))\n",
    "solar_model.add(Dense(128))\n",
    "solar_model.add(BatchNormalization())\n",
    "solar_model.add(Dense(128))\n",
    "solar_model.add(BatchNormalization())\n",
    "solar_model.add(Dense(128))\n",
    "solar_model.add(BatchNormalization())\n",
    "solar_model.add(MixtureDensity(-1, 1))\n",
    "solar_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compiling the model by passing adam optimizer, loss=MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "191/191 [==============================] - 6s 11ms/step - loss: 0.7304 - val_loss: 0.3697\n",
      "Epoch 2/15\n",
      "191/191 [==============================] - 0s 2ms/step - loss: 0.1498 - val_loss: 0.1755\n",
      "Epoch 3/15\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.1164 - val_loss: 0.1934\n",
      "Epoch 4/15\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.1047 - val_loss: 0.1042\n",
      "Epoch 5/15\n",
      "191/191 [==============================] - 0s 2ms/step - loss: 0.1102 - val_loss: 0.0886\n",
      "Epoch 6/15\n",
      "191/191 [==============================] - 0s 2ms/step - loss: 0.1038 - val_loss: 0.1086\n",
      "Epoch 7/15\n",
      "191/191 [==============================] - 0s 2ms/step - loss: 0.0930 - val_loss: 0.0904\n",
      "Epoch 8/15\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.0925 - val_loss: 0.1130\n",
      "Epoch 9/15\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.0904 - val_loss: 0.0974\n",
      "Epoch 10/15\n",
      "191/191 [==============================] - 1s 4ms/step - loss: 0.1040 - val_loss: 0.1172\n",
      "Epoch 11/15\n",
      "191/191 [==============================] - 1s 4ms/step - loss: 0.0834 - val_loss: 0.0885\n",
      "Epoch 12/15\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.0874 - val_loss: 0.1004\n",
      "Epoch 13/15\n",
      "191/191 [==============================] - 0s 3ms/step - loss: 0.0898 - val_loss: 0.0825\n",
      "Epoch 14/15\n",
      "191/191 [==============================] - 0s 2ms/step - loss: 0.0789 - val_loss: 0.0700\n",
      "Epoch 15/15\n",
      "191/191 [==============================] - 0s 2ms/step - loss: 0.0749 - val_loss: 0.1036\n"
     ]
    }
   ],
   "source": [
    "solar_model.compile(loss='mean_squared_error',optimizer='adam')\n",
    "history = solar_model.fit(X_solar_train, y_solar_train, epochs=15, validation_data = (X_solar_valid, y_solar_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41/41 [==============================] - 0s 925us/step - loss: 0.2079\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.2078564614057541"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar_model.evaluate(X_solar_test, y_solar_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Keras Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(\n",
    "    solar_model, to_file='figs/6-model.png', show_shapes=True, show_dtype=False,\n",
    "    show_layer_names=True, rankdir='TB', expand_nested=False, dpi=96\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Model's Loss Metrics over Train and Validation sets for Solar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Actual vs Predictions on the same plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_solar_pred = solar_model.predict(X_solar_train)\n",
    "val_solar_pred = solar_model.predict(X_solar_valid)\n",
    "test_solar_pred = solar_model.predict(X_solar_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = thesis_dataset[['DE_solar_generation_actual']]\n",
    "\n",
    "train_df = train[['DE_solar_generation_actual']]\n",
    "train_df.loc[:, 'DE_solar_generation_actual'] = scaler.inverse_transform(train_solar_pred)\n",
    "\n",
    "valid_df = valid[['DE_solar_generation_actual']]\n",
    "valid_df.loc[:, 'DE_solar_generation_actual'] = scaler.inverse_transform(val_solar_pred)\n",
    "\n",
    "test_df = test[['DE_solar_generation_actual']]\n",
    "test_df.loc[:, 'DE_solar_generation_actual'] = scaler.inverse_transform(test_solar_pred)\n",
    "\n",
    "# Plot all predictions\n",
    "inversetransform, =plt.plot(df, label = 'Actual')\n",
    "train_solar_predx, =plt.plot(train_df, color='orange', label = 'Train')\n",
    "val_solar_predx, =plt.plot(valid_df, color='green', label = 'Validation')\n",
    "test_solar_predx, =plt.plot(test_df, color='black', label = 'Test')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Solar Output')\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.title(\"Predicted vs. Actual Solar Generation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Metrics for Solar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</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>drnn</th>\n",
       "      <td>1193.563703</td>\n",
       "      <td>2639.660126</td>\n",
       "      <td>51.377623</td>\n",
       "      <td>3.939203</td>\n",
       "      <td>-0.256206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               MAE          MSE       RMSE     RMSLE        R2\n",
       "model                                                         \n",
       "drnn   1193.563703  2639.660126  51.377623  3.939203 -0.256206"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics('drnn', test[['DE_solar_generation_actual']], test_solar_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling and Fitting the model for Wind Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "batch_normalization_4 (Batch (None, 4)                 16        \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 128)               640       \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "mixture_density_1 (MixtureDe (None, 1)                 3121      \n",
      "=================================================================\n",
      "Total params: 38,337\n",
      "Trainable params: 37,561\n",
      "Non-trainable params: 776\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "wind_model = Sequential()\n",
    "wind_model.add(BatchNormalization(input_shape=(4,)))\n",
    "wind_model.add(Dense(128))\n",
    "wind_model.add(BatchNormalization())\n",
    "wind_model.add(Dense(128))\n",
    "wind_model.add(BatchNormalization())\n",
    "wind_model.add(Dense(128))\n",
    "wind_model.add(BatchNormalization())\n",
    "wind_model.add(MixtureDensity(-1, 1))\n",
    "wind_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "191/191 [==============================] - 7s 9ms/step - loss: 1.2306 - val_loss: 0.5360\n",
      "Epoch 2/20\n",
      "191/191 [==============================] - 2s 8ms/step - loss: 0.3220 - val_loss: 0.3468\n",
      "Epoch 3/20\n",
      "191/191 [==============================] - 2s 8ms/step - loss: 0.2048 - val_loss: 0.2907\n",
      "Epoch 4/20\n",
      "191/191 [==============================] - 2s 8ms/step - loss: 0.1979 - val_loss: 0.3458\n",
      "Epoch 5/20\n",
      "191/191 [==============================] - 2s 8ms/step - loss: 0.1932 - val_loss: 0.2357\n",
      "Epoch 6/20\n",
      "191/191 [==============================] - 2s 10ms/step - loss: 0.1681 - val_loss: 0.2691\n",
      "Epoch 7/20\n",
      "191/191 [==============================] - 2s 9ms/step - loss: 0.1727 - val_loss: 0.2209 - ETA: 0s - l\n",
      "Epoch 8/20\n",
      "191/191 [==============================] - 1s 5ms/step - loss: 0.1551 - val_loss: 0.1886\n",
      "Epoch 9/20\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.1522 - val_loss: 0.1748\n",
      "Epoch 10/20\n",
      "191/191 [==============================] - 0s 3ms/step - loss: 0.1484 - val_loss: 0.2564\n",
      "Epoch 11/20\n",
      "191/191 [==============================] - 0s 2ms/step - loss: 0.1669 - val_loss: 0.1603\n",
      "Epoch 12/20\n",
      "191/191 [==============================] - 0s 2ms/step - loss: 0.1406 - val_loss: 0.2211\n",
      "Epoch 13/20\n",
      "191/191 [==============================] - 0s 2ms/step - loss: 0.1379 - val_loss: 0.1472\n",
      "Epoch 14/20\n",
      "191/191 [==============================] - 1s 5ms/step - loss: 0.1371 - val_loss: 0.2428\n",
      "Epoch 15/20\n",
      "191/191 [==============================] - 1s 4ms/step - loss: 0.1350 - val_loss: 0.2037\n",
      "Epoch 16/20\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.1325 - val_loss: 0.1970\n",
      "Epoch 17/20\n",
      "191/191 [==============================] - 0s 2ms/step - loss: 0.1216 - val_loss: 0.1380\n",
      "Epoch 18/20\n",
      "191/191 [==============================] - 0s 2ms/step - loss: 0.1313 - val_loss: 0.1826\n",
      "Epoch 19/20\n",
      "191/191 [==============================] - 0s 2ms/step - loss: 0.1322 - val_loss: 0.1672\n",
      "Epoch 20/20\n",
      "191/191 [==============================] - 0s 2ms/step - loss: 0.1325 - val_loss: 0.1580\n"
     ]
    }
   ],
   "source": [
    "wind_model.compile(loss='mean_squared_error',optimizer='adam')\n",
    "history = wind_model.fit(X_wind_train, y_wind_train, validation_data=(X_wind_valid, y_wind_valid), epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41/41 [==============================] - 0s 750us/step - loss: 0.1357\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.13573773205280304"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wind_model.evaluate(X_wind_test, y_wind_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model's Loss for Wind Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Actual vs Predicted for Wind on same graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_wind_pred = wind_model.predict(X_wind_train)\n",
    "val_wind_pred = wind_model.predict(X_wind_valid)\n",
    "test_wind_pred = wind_model.predict(X_wind_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = thesis_dataset[['DE_wind_generation_actual']]\n",
    "\n",
    "train_df = train[['DE_wind_generation_actual']]\n",
    "train_df.loc[:, 'DE_wind_generation_actual'] = scaler.inverse_transform(train_wind_pred)\n",
    "\n",
    "valid_df = valid[['DE_wind_generation_actual']]\n",
    "valid_df.loc[:, 'DE_wind_generation_actual'] = scaler.inverse_transform(val_wind_pred)\n",
    "\n",
    "test_df = test[['DE_wind_generation_actual']]\n",
    "test_df.loc[:, 'DE_wind_generation_actual'] = scaler.inverse_transform(test_wind_pred)\n",
    "\n",
    "# Plot all predictions\n",
    "inversetransform, =plt.plot(df, label = 'Actual')\n",
    "train_wind_predx, =plt.plot(train_df, color='orange', label = 'Train')\n",
    "val_wind_predx, =plt.plot(valid_df, color='green', label = 'Validation')\n",
    "test_wind_predx, =plt.plot(test_df, color='black', label = 'Test')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Wind Output')\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.title(\"Predicted vs. Actual Wind Generation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Metrics for Wind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>R2</th>\n",
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       "      <th>drnn</th>\n",
       "      <td>2456.978201</td>\n",
       "      <td>3184.883815</td>\n",
       "      <td>56.434775</td>\n",
       "      <td>4.033086</td>\n",
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      "text/plain": [
       "               MAE          MSE       RMSE     RMSLE        R2\n",
       "model                                                         \n",
       "drnn   2456.978201  3184.883815  56.434775  4.033086  0.864262"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics('drnn', test[['DE_wind_generation_actual']], scaler.inverse_transform(test_wind_pred))"
   ]
  }
 ],
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