3. CNN + LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting Renewable Energy Output Using Long-Short Term Memory and Convolutional Neural Networks"
   ]
  },
  {
   "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 required for building CNN + LSTM model\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.layers import Conv1D, BatchNormalization, Input, Dense, Flatten, LSTM, Reshape, TimeDistributed\n",
    "from tensorflow.keras.models import Model\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 = train[['SWTDN', 'SWGDN', 'T', 'p']]\n",
    "y_solar_train = train['DE_solar_generation_actual']\n",
    "X_solar_valid = valid[['SWTDN', 'SWGDN', 'T', 'p']]\n",
    "y_solar_valid = valid['DE_solar_generation_actual']\n",
    "X_solar_test = test[['SWTDN', 'SWGDN', 'T', 'p']]\n",
    "y_solar_test = test['DE_solar_generation_actual']\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": [
    "## Building a CNN + LSTM TensorFlow Model using Conv1D and LSTM for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 4)]               0         \n",
      "_________________________________________________________________\n",
      "reshape (Reshape)            (None, 4, 1)              0         \n",
      "_________________________________________________________________\n",
      "conv1d (Conv1D)              (None, 3, 128)            384       \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 3, 128)            512       \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 2, 128)            32896     \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 2, 128)            512       \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 1, 128)            32896     \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 1, 128)            512       \n",
      "_________________________________________________________________\n",
      "time_distributed (TimeDistri (None, 1, 128)            0         \n",
      "_________________________________________________________________\n",
      "lstm (LSTM)                  (None, 1024)              4722688   \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1024)              1049600   \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 1024)              4096      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 1025      \n",
      "=================================================================\n",
      "Total params: 5,845,121\n",
      "Trainable params: 5,842,305\n",
      "Non-trainable params: 2,816\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Input layer describing the dimension in which the data will be passed\n",
    "digit_a = Input(shape=(4,))\n",
    "#Reshape helps in converting row * col wise data into only col wise data for Conv layer to process\n",
    "x = Reshape((-1,1))(digit_a)\n",
    "#Building Conv layers of 128 filters and 2 kernel_size with BatchNormalization\n",
    "x = Conv1D(128, 2)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Conv1D(128, 2)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Conv1D(128, 2)(x)\n",
    "x = BatchNormalization()(x)\n",
    "#Flattening the output into timedistributed fashion for LSTM to process\n",
    "out_a = TimeDistributed(Flatten())(x)\n",
    "\n",
    "#LSTM layer with 1024 units and with no return_sequences\n",
    "out = LSTM(1024, input_shape = train.shape, return_sequences=False)(out_a)\n",
    "#Dense layer of 1024 that captures the output of LSTM layer\n",
    "out = Dense(1024)(out)\n",
    "#BatchNormalization followed by Dense of 1 layer to get the output of the model\n",
    "out = BatchNormalization()(out)\n",
    "out = Dense(1)(out)\n",
    "#Create the layer and print the summary\n",
    "solar_model = Model(digit_a, out)\n",
    "solar_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compiling the model by passing adam optimizer, loss=MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "191/191 [==============================] - 13s 55ms/step - loss: 4747.1050 - val_loss: 3125.9412\n",
      "Epoch 2/2\n",
      "191/191 [==============================] - 10s 52ms/step - loss: 4577.3689 - val_loss: 2885.9473\n"
     ]
    }
   ],
   "source": [
    "solar_model.compile(optimizer = 'adam', loss = 'mean_absolute_error')\n",
    "history = solar_model.fit(X_solar_train, y_solar_train, validation_data=(X_solar_valid, y_solar_valid), epochs=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41/41 [==============================] - 0s 6ms/step - loss: 1117.5289\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1117.5289306640625"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar_model.evaluate(X_solar_test, y_solar_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the keras model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(\n",
    "    solar_model, to_file='figs/3-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": 7,
   "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": 8,
   "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": 9,
   "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'] = train_solar_pred\n",
    "\n",
    "valid_df = valid[['DE_solar_generation_actual']]\n",
    "valid_df.loc[:, 'DE_solar_generation_actual'] = val_solar_pred\n",
    "\n",
    "test_df = test[['DE_solar_generation_actual']]\n",
    "test_df.loc[:, 'DE_solar_generation_actual'] = 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": 10,
   "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>cnn+lstm</th>\n",
       "      <td>1117.528983</td>\n",
       "      <td>2431.138122</td>\n",
       "      <td>49.306573</td>\n",
       "      <td>3.898057</td>\n",
       "      <td>-0.065575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  MAE          MSE       RMSE     RMSLE        R2\n",
       "model                                                            \n",
       "cnn+lstm  1117.528983  2431.138122  49.306573  3.898057 -0.065575"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics('cnn+lstm', test[['DE_solar_generation_actual']], test_solar_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the model for Wind Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 4)]               0         \n",
      "_________________________________________________________________\n",
      "reshape (Reshape)            (None, 4, 1)              0         \n",
      "_________________________________________________________________\n",
      "conv1d (Conv1D)              (None, 3, 128)            384       \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 3, 128)            512       \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 2, 128)            32896     \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 2, 128)            512       \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 1, 128)            32896     \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 1, 128)            512       \n",
      "_________________________________________________________________\n",
      "time_distributed (TimeDistri (None, 1, 128)            0         \n",
      "_________________________________________________________________\n",
      "lstm (LSTM)                  (None, 1024)              4722688   \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1024)              1049600   \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 1024)              4096      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 1025      \n",
      "=================================================================\n",
      "Total params: 5,845,121\n",
      "Trainable params: 5,842,305\n",
      "Non-trainable params: 2,816\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Input layer describing the dimension in which the data will be passed\n",
    "digit_a = Input(shape=(4,))\n",
    "#Reshape helps in converting row * col wise data into only col wise data for Conv layer to process\n",
    "x = Reshape((-1,1))(digit_a)\n",
    "#Building Conv layers of 128 filters and 2 kernel_size with BatchNormalization\n",
    "x = Conv1D(128, 2)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Conv1D(128, 2)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Conv1D(128, 2)(x)\n",
    "x = BatchNormalization()(x)\n",
    "#Flattening the output into timedistributed fashion for LSTM to process\n",
    "out_a = TimeDistributed(Flatten())(x)\n",
    "\n",
    "#LSTM layer with 1024 units and with no return_sequences\n",
    "out = LSTM(1024, input_shape = train.shape, return_sequences=False)(out_a)\n",
    "#Dense layer of 1024 that captures the output of LSTM layer\n",
    "out = Dense(1024)(out)\n",
    "#BatchNormalization followed by Dense of 1 layer to get the output of the model\n",
    "out = BatchNormalization()(out)\n",
    "out = Dense(1)(out)\n",
    "#Create the layer and print the summary\n",
    "wind_model = Model(digit_a, out)\n",
    "solar_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting the same model for Wind Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "191/191 [==============================] - 13s 56ms/step - loss: 1.1210 - val_loss: 0.4604\n",
      "Epoch 2/2\n",
      "191/191 [==============================] - 10s 50ms/step - loss: 0.3268 - val_loss: 0.4636\n"
     ]
    }
   ],
   "source": [
    "wind_model.compile(optimizer = 'adam', loss = 'mean_absolute_error')\n",
    "history = wind_model.fit(X_wind_train, y_wind_train, validation_data=(X_wind_valid, y_wind_valid), epochs=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41/41 [==============================] - 0s 11ms/step - loss: 0.4910\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.490988552570343"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "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": 15,
   "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": 16,
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cnn+lstm</th>\n",
       "      <td>4244.384262</td>\n",
       "      <td>5606.431963</td>\n",
       "      <td>74.876111</td>\n",
       "      <td>4.315835</td>\n",
       "      <td>0.579383</td>\n",
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      "text/plain": [
       "                  MAE          MSE       RMSE     RMSLE        R2\n",
       "model                                                            \n",
       "cnn+lstm  4244.384262  5606.431963  74.876111  4.315835  0.579383"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics('cnn+lstm', test[['DE_wind_generation_actual']], scaler.inverse_transform(test_wind_pred))"
   ]
  }
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