├── README.md ├── data └── data.txt ├── LICENSE └── STLF_LSTM.ipynb /README.md: -------------------------------------------------------------------------------- 1 | # STLF-BiLSTM-CNNBiLSTM 2 | Short-Term Aggregated Residential Load Forecasting using BiLSTM and CNN-BiLSTM 3 | -------------------------------------------------------------------------------- /data/data.txt: -------------------------------------------------------------------------------- 1 | The publicly available dataset used in this work can be accessed from this link: https://zenodo.org/record/5642902#.Y-XnInbMKUl 2 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 Bharat Bohara 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /STLF_LSTM.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "id": "46StCIqsE4i_" 7 | }, 8 | "source": [ 9 | "# Short-term Load Forecasting using LSTM\n", 10 | "\n", 11 | "\n", 12 | "---\n", 13 | "\n" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": null, 19 | "metadata": { 20 | "colab": { 21 | "base_uri": "https://localhost:8080/" 22 | }, 23 | "id": "bKImEp-ZEwPn", 24 | "outputId": "6d635294-b344-4445-905d-81cc7d6993de" 25 | }, 26 | "outputs": [ 27 | { 28 | "name": "stdout", 29 | "output_type": "stream", 30 | "text": [ 31 | "Mounted at /content/drive\n" 32 | ] 33 | } 34 | ], 35 | "source": [ 36 | "# from google.colab import drive\n", 37 | "# drive.mount('/content/drive')" 38 | ] 39 | }, 40 | { 41 | "cell_type": "markdown", 42 | "metadata": { 43 | "id": "EmXABzBDFL3y" 44 | }, 45 | "source": [ 46 | "## Import Libraries" 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": null, 52 | "metadata": { 53 | "colab": { 54 | "base_uri": "https://localhost:8080/" 55 | }, 56 | "id": "6hBk_UwyE3i_", 57 | "outputId": "28aeb93f-19e3-40a5-f84a-b5fe8b45cde9" 58 | }, 59 | "outputs": [ 60 | { 61 | "name": "stderr", 62 | "output_type": "stream", 63 | "text": [ 64 | "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", 65 | " import pandas.util.testing as tm\n" 66 | ] 67 | } 68 | ], 69 | "source": [ 70 | "import numpy as np\n", 71 | "from math import sqrt\n", 72 | "from pandas import read_csv\n", 73 | "import matplotlib.pyplot as plt\n", 74 | "\n", 75 | "from sklearn.metrics import mean_squared_error\n", 76 | "from sklearn.preprocessing import MinMaxScaler\n", 77 | "from scipy.interpolate import make_interp_spline\n", 78 | "\n", 79 | "from keras.callbacks import CSVLogger\n", 80 | "from keras.models import Sequential\n", 81 | "from keras.layers import Dense\n", 82 | "from keras.layers import LSTM\n", 83 | "from keras.layers import TimeDistributed\n", 84 | "from keras.layers import Bidirectional\n", 85 | "from keras.layers.convolutional import Conv1D\n", 86 | "from keras.layers.convolutional import MaxPooling1D\n", 87 | "from keras.layers import Flatten\n", 88 | "from keras.layers import RepeatVector\n", 89 | "from keras.backend import dropout\n", 90 | "\n", 91 | "from statsmodels.tsa.seasonal import seasonal_decompose" 92 | ] 93 | }, 94 | { 95 | "cell_type": "markdown", 96 | "metadata": { 97 | "id": "qE8QmwJvFUvA" 98 | }, 99 | "source": [ 100 | "## Helper Functions" 101 | ] 102 | }, 103 | { 104 | "cell_type": "code", 105 | "execution_count": null, 106 | "metadata": { 107 | "id": "IrJiOxiBFQmP" 108 | }, 109 | "outputs": [], 110 | "source": [ 111 | "# split dataset into train, validate and test sets\n", 112 | "def split_dataset(data):\n", 113 | "\t# split into train validation and test sets\n", 114 | "\ttrain, val, test = data[12:13932], data [13932:18300], data[18300:22716]\n", 115 | "\t# restructure into samples of daily data shape is [samples, hours, feature]\n", 116 | "\ttrain = np.array(np.split(train, len(train)/24))\n", 117 | "\tval = np.array(np.split(val, len(val)/24))\n", 118 | "\ttest = np.array(np.split(test, len(test)/24))\n", 119 | "\treturn train, val, test\n", 120 | "\n", 121 | "# evaluate one or more daily forecasts against expected values\n", 122 | "def evaluate_forecasts(actual, predicted):\n", 123 | "\tscores = list()\n", 124 | "\t# calculate an RMSE score for each hour of the day\n", 125 | "\tfor i in range(actual.shape[1]):\n", 126 | "\t\t# calculate mse\n", 127 | "\t\tmse = mean_squared_error(actual[:, i], predicted[:, i])\n", 128 | "\t\t# calculate rmse\n", 129 | "\t\trmse = sqrt(mse)\n", 130 | "\t\t# store in scores list\n", 131 | "\t\tscores.append(rmse)\n", 132 | " \n", 133 | "\t# calculate overall RMSE\n", 134 | "\ts = 0\n", 135 | "\tfor row in range(actual.shape[0]):\n", 136 | "\t\tfor col in range(actual.shape[1]):\n", 137 | "\t\t\ts += (actual[row, col] - predicted[row, col])**2\n", 138 | "\tscore = sqrt(s / (actual.shape[0] * actual.shape[1]))\n", 139 | "\treturn score, scores\n", 140 | "\n", 141 | "# summarize scores\n", 142 | "def summarize_scores(name, score, scores):\n", 143 | "\ts_scores = ', '.join(['%.1f' % s for s in scores])\n", 144 | "\tprint('%s: [%.3f] %s' % (name, score, s_scores))\n", 145 | "\n", 146 | "# convert history into inputs (actual) and outputs (predicted) for training\n", 147 | "def convert_train_val(train, n_input, n_out=24):\n", 148 | "\t# flatten data\n", 149 | "\tdata = train.reshape((train.shape[0]*train.shape[1], train.shape[2]))\n", 150 | "\tX, y = list(), list()\n", 151 | "\tin_start = 0\n", 152 | "\t# step over the entire history one time step at a time\n", 153 | "\tfor _ in range(len(data)):\n", 154 | "\t\t# define the end of the input sequence\n", 155 | "\t\tin_end = in_start + n_input\n", 156 | "\t\tout_end = in_end + n_out\n", 157 | "\t\t# ensure we have enough data for this instance\n", 158 | "\t\tif out_end <= len(data):\n", 159 | "\t\t\tx_input = data[in_start:in_end, 0]\n", 160 | "\t\t\tx_input = x_input.reshape((len(x_input), 1))\n", 161 | "\t\t\tX.append(x_input)\n", 162 | "\t\t\ty.append(data[in_end:out_end, 0])\n", 163 | "\t\t# move along one time step of 1 hour\n", 164 | "\t\tin_start += 1\n", 165 | "\treturn np.array(X), np.array(y)\n", 166 | "\n", 167 | "\n", 168 | "# train the model\n", 169 | "def build_model_LSTM(train, val, n_input):\n", 170 | "\t# Create training and validation sets\n", 171 | "\ttrain_x, train_y = convert_train_val(train, n_input)\n", 172 | "\tval_x, val_y = convert_train_val(val, n_input)\n", 173 | "\t# define parameters\n", 174 | "\tcsv_logger = CSVLogger('LSTM_Model_Logger.log')\n", 175 | "\tverbose, epochs, batch_size = 0, 30, 384\n", 176 | "\tn_timesteps, n_features, n_outputs = train_x.shape[1], train_x.shape[2], train_y.shape[1]\n", 177 | "\tnv_timesteps, nv_features, nv_outputs = val_x.shape[1], val_x.shape[2], val_y.shape[1]\n", 178 | "\t# reshape output into [samples, timesteps, features]\n", 179 | "\ttrain_y = train_y.reshape((train_y.shape[0], train_y.shape[1], 1))\n", 180 | "\tval_y = val_y.reshape((val_y.shape[0], val.shape[1], 1))\n", 181 | " \n", 182 | "\t# define model\n", 183 | "\tmodel = Sequential()\n", 184 | "\tmodel.add(LSTM(100, activation='relu', input_shape=(n_timesteps, n_features)))\n", 185 | "\tmodel.add(Dense(100, activation='relu'))\n", 186 | "\tmodel.add(Dense(n_outputs))\n", 187 | "\tmodel.compile(loss='mse', optimizer='adam')\n", 188 | "\tprint(model.summary())\n", 189 | "\t# fit network\n", 190 | "\thist=model.fit(train_x, train_y, validation_data=(val_x, val_y),callbacks=[csv_logger], epochs=epochs, batch_size=batch_size, verbose=verbose)\n", 191 | "\treturn model, hist\n", 192 | "\n", 193 | "# make a forecast\n", 194 | "def forecast(model, history, n_input):\n", 195 | "\t# flatten data\n", 196 | "\tdata = np.array(history)\n", 197 | "\tdata = data.reshape((data.shape[0]*data.shape[1], data.shape[2]))\n", 198 | "\t# retrieve last observations for input data\n", 199 | "\tinput_x = data[-n_input:, 0]\n", 200 | "\t# reshape into [1, n_input, 1] that is 1 sample, sample data size, one feature\n", 201 | "\tinput_x = input_x.reshape((1, len(input_x), 1))\n", 202 | "\t# forecast the next hour\n", 203 | "\tyhat = model.predict(input_x, verbose=0)\n", 204 | "\t# we only want the vector forecast\n", 205 | "\tyhat = yhat[0]\n", 206 | "\treturn yhat\n", 207 | "\n", 208 | "# evaluate model fits model using training and validation data, then conducts a walk forwad validation with the test data\n", 209 | "def evaluate_model_LSTM(train, val, test, n_input):\n", 210 | "\t# fit model\n", 211 | "\tmodel, hist = build_model_LSTM(train, val, n_input)\n", 212 | "\t# history is a list of houry data\n", 213 | "\thistory = [x for x in train]\n", 214 | "\t# walk-forward validation over each hour in test set\n", 215 | "\tpredictions = list()\n", 216 | "\tfor i in range(len(test)):\n", 217 | "\t\t# predict the hour\n", 218 | "\t\tyhat_sequence = forecast(model, history, n_input)\n", 219 | "\t\t# store the predictions\n", 220 | "\t\tpredictions.append(yhat_sequence)\n", 221 | "\t\t# get real observation and add to history for predicting the next hour\n", 222 | "\t\thistory.append(test[i, :])\n", 223 | "\t# evaluate predictions hours for each day\n", 224 | "\tpredictions = np.array(predictions)\n", 225 | "\tscore, scores = evaluate_forecasts(test[:, :, 0], predictions)\n", 226 | "\treturn score, scores, hist, predictions" 227 | ] 228 | }, 229 | { 230 | "cell_type": "markdown", 231 | "metadata": { 232 | "id": "XJvDoo1oFsGr" 233 | }, 234 | "source": [ 235 | "## Load Dataset" 236 | ] 237 | }, 238 | { 239 | "cell_type": "code", 240 | "execution_count": null, 241 | "metadata": { 242 | "id": "9xgQmueqFpWd" 243 | }, 244 | "outputs": [], 245 | "source": [ 246 | "dataset = read_csv('data/data_spatial_TotalKW.csv', header=0, \n", 247 | " infer_datetime_format=True, parse_dates=['datetime'], index_col=['datetime'])" 248 | ] 249 | }, 250 | { 251 | "cell_type": "markdown", 252 | "metadata": { 253 | "id": "xLpk9d0RFz4l" 254 | }, 255 | "source": [ 256 | "#### Decompose data into trend, seasonality, and noise" 257 | ] 258 | }, 259 | { 260 | "cell_type": "code", 261 | "execution_count": null, 262 | "metadata": { 263 | "colab": { 264 | "base_uri": "https://localhost:8080/", 265 | "height": 319 266 | }, 267 | "id": "kxES9bK_Fw2-", 268 | "outputId": "f9946e64-061c-44bf-d3e8-85b490ff994c" 269 | }, 270 | "outputs": [ 271 | { 272 | "data": { 273 | "text/plain": [ 274 | "
" 275 | ] 276 | }, 277 | "metadata": {}, 278 | "output_type": "display_data" 279 | }, 280 | { 281 | "data": { 282 | "image/png": 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", 283 | "text/plain": [ 284 | "
" 285 | ] 286 | }, 287 | "metadata": {}, 288 | "output_type": "display_data" 289 | } 290 | ], 291 | "source": [ 292 | "fig1 = plt.gcf()\n", 293 | "month = dataset.iloc[0:720]\n", 294 | "decomp = seasonal_decompose(month, model='additive')\n", 295 | "decomp.plot()\n", 296 | "plt.show()\n", 297 | "fig1.savefig('seasonal_decompose.pdf')" 298 | ] 299 | }, 300 | { 301 | "cell_type": "markdown", 302 | "metadata": { 303 | "id": "VSxxkgV5F9X6" 304 | }, 305 | "source": [ 306 | "#### Normalization - normalize data using min-max scaling" 307 | ] 308 | }, 309 | { 310 | "cell_type": "code", 311 | "execution_count": null, 312 | "metadata": { 313 | "colab": { 314 | "base_uri": "https://localhost:8080/" 315 | }, 316 | "id": "Qw5pn-ceF6Kd", 317 | "outputId": "8a649ecd-9771-46da-c383-2d3f1a98664b" 318 | }, 319 | "outputs": [ 320 | { 321 | "name": "stdout", 322 | "output_type": "stream", 323 | "text": [ 324 | "train size: (580, 24, 1)\n", 325 | "valid size: (182, 24, 1)\n", 326 | "test size : (184, 24, 1)\n" 327 | ] 328 | } 329 | ], 330 | "source": [ 331 | "trans = MinMaxScaler()\n", 332 | "dataset = trans.fit_transform(dataset)\n", 333 | "tran_scale = trans.scale_\n", 334 | "\n", 335 | "# split into train and test\n", 336 | "train, val, test = split_dataset(dataset)\n", 337 | "print('train size: ', train.shape)\n", 338 | "print('valid size: ', val.shape)\n", 339 | "print('test size : ', test.shape)" 340 | ] 341 | }, 342 | { 343 | "cell_type": "markdown", 344 | "metadata": { 345 | "id": "RTO6Tp-_GHPV" 346 | }, 347 | "source": [ 348 | "#### Actual data" 349 | ] 350 | }, 351 | { 352 | "cell_type": "code", 353 | "execution_count": null, 354 | "metadata": { 355 | "id": "fmzHN6OvGDTI" 356 | }, 357 | "outputs": [], 358 | "source": [ 359 | "N_actual= test[1:184] / tran_scale" 360 | ] 361 | }, 362 | { 363 | "cell_type": "markdown", 364 | "metadata": { 365 | "id": "tmFVYvSSGQ2D" 366 | }, 367 | "source": [ 368 | "#### Evaulate LSTM model and get scores" 369 | ] 370 | }, 371 | { 372 | "cell_type": "code", 373 | "execution_count": null, 374 | "metadata": { 375 | "colab": { 376 | "background_save": true, 377 | "base_uri": "https://localhost:8080/" 378 | }, 379 | "id": "5YJwh1uEGKAC", 380 | "outputId": "65bee63f-933e-4494-f431-c2e265a9912f" 381 | }, 382 | "outputs": [ 383 | { 384 | "name": "stdout", 385 | "output_type": "stream", 386 | "text": [ 387 | "Model: \"sequential\"\n", 388 | "_________________________________________________________________\n", 389 | " Layer (type) Output Shape Param # \n", 390 | "=================================================================\n", 391 | " lstm (LSTM) (None, 100) 40800 \n", 392 | " \n", 393 | " dense (Dense) (None, 100) 10100 \n", 394 | " \n", 395 | " dense_1 (Dense) (None, 24) 2424 \n", 396 | " \n", 397 | "=================================================================\n", 398 | "Total params: 53,324\n", 399 | "Trainable params: 53,324\n", 400 | "Non-trainable params: 0\n", 401 | "_________________________________________________________________\n", 402 | "None\n", 403 | "rmse: [1.539] 0.5, 0.5, 0.5, 0.7, 1.1, 1.3, 1.4, 1.7, 1.9, 2.1, 2.6, 2.1, 1.9, 1.8, 1.8, 1.8, 2.3, 1.8, 1.4, 1.3, 1.1, 1.0, 0.8, 0.7\n" 404 | ] 405 | }, 406 | { 407 | "data": { 408 | "image/png": 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", 409 | "text/plain": [ 410 | "
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", 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", 440 | "text/plain": [ 441 | "
" 442 | ] 443 | }, 444 | "metadata": {}, 445 | "output_type": "display_data" 446 | } 447 | ], 448 | "source": [ 449 | "n_input = 24\n", 450 | "LSTM_score, LSTM_scores, hist, predictions_LSTM = evaluate_model_LSTM(train, val, test, n_input)\n", 451 | "LSTM_score, LSTM_scores, predictions_LSTM = LSTM_score / tran_scale , LSTM_scores / tran_scale, predictions_LSTM / tran_scale\n", 452 | "# summarize rmse scores\n", 453 | "summarize_scores('rmse', LSTM_score, LSTM_scores)\n", 454 | "\n", 455 | "# plot rmse scores\n", 456 | "fig10 = plt.gcf()\n", 457 | "hours = ['1', '2', '3', '4', '5', '6', '7','8', '9', '10', '11', '12','13', '14', '15', '16', '17', '18', '19','20', '21', '22', '23', '24' ]\n", 458 | "plt.plot(hours, LSTM_scores, marker='o', label='lstm')\n", 459 | "plt.xlabel(\"Hour\")\n", 460 | "plt.ylabel(\"RMSE [kW]\")\n", 461 | "plt.show()\n", 462 | "fig10.savefig('rmse-LSTM.pdf')\n", 463 | "print(LSTM_score)\n", 464 | "print(LSTM_scores)\n", 465 | "\n", 466 | "# plot Training and Validation Loss\n", 467 | "fig11 = plt.gcf()\n", 468 | "loss = hist.history['loss']\n", 469 | "val_loss = hist.history['val_loss']\n", 470 | "epochs = range(1, len(loss) + 1)\n", 471 | "plt.plot(epochs, loss, 'b', label='Training loss')\n", 472 | "plt.plot(epochs, val_loss, 'r', label='Validation loss')\n", 473 | "plt.title('Training and Validation Loss')\n", 474 | "plt.xlabel(\"Epoch\")\n", 475 | "plt.ylabel(\"MSE\")\n", 476 | "plt.legend()\n", 477 | "plt.show()\n", 478 | "fig11.savefig('train-val-loss.pdf')\n", 479 | "\n", 480 | "# plot an example prediction for one day\n", 481 | "fig12 = plt.gcf()\n", 482 | "plt.plot(hours, N_actual[15], 'b', label='Actual')\n", 483 | "plt.plot(hours, predictions_LSTM[15], 'r', label='Predicted')\n", 484 | "plt.title('24 hr Actual vs Predicted')\n", 485 | "plt.xlabel(\"Hour\")\n", 486 | "plt.ylabel(\"Active Power [kW]\")\n", 487 | "plt.legend()\n", 488 | "plt.show()\n", 489 | "fig12.savefig('predictions-LSTM.pdf')" 490 | ] 491 | } 492 | ], 493 | "metadata": { 494 | "accelerator": "GPU", 495 | "colab": { 496 | "provenance": [] 497 | }, 498 | "kernelspec": { 499 | "display_name": "Python 3", 500 | "name": "python3" 501 | }, 502 | "language_info": { 503 | "name": "python" 504 | } 505 | }, 506 | "nbformat": 4, 507 | "nbformat_minor": 0 508 | } 509 | --------------------------------------------------------------------------------