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{
"cells": [
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd \n",
"from datetime import datetime \n",
"from datetime import date\n",
"import matplotlib.pyplot as plt\n",
"# import seaborn as sns\n",
"import numpy as np\n",
"import pandas as pd\n",
"from keras.models import Sequential\n",
"from keras.layers import LSTM, Dense\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import MinMaxScaler,StandardScaler\n",
"from keras.callbacks import ModelCheckpoint\n",
"\n",
"dataPATH = r\"C:\\Users\\levim\\OneDrive\\Documents\\MastersAI_ES\\TeamProject-5ARIP10\\smart-buildings\\Data\"\n",
"all_data = pd.read_csv(dataPATH + r\"\\long_merge.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 102,
"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>date</th>\n",
" <th>hvac_N</th>\n",
" <th>hvac_S</th>\n",
" <th>air_temp_set_1</th>\n",
" <th>solar_radiation_set_1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2018-01-01 00:00:00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>11.64</td>\n",
" <td>86.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2018-01-01 00:01:00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>11.64</td>\n",
" <td>86.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2018-01-01 00:02:00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>11.64</td>\n",
" <td>86.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2018-01-01 00:03:00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>11.64</td>\n",
" <td>86.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2018-01-01 00:04:00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>11.64</td>\n",
" <td>86.7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date hvac_N hvac_S air_temp_set_1 solar_radiation_set_1\n",
"0 2018-01-01 00:00:00 NaN NaN 11.64 86.7\n",
"1 2018-01-01 00:01:00 NaN NaN 11.64 86.7\n",
"2 2018-01-01 00:02:00 NaN NaN 11.64 86.7\n",
"3 2018-01-01 00:03:00 NaN NaN 11.64 86.7\n",
"4 2018-01-01 00:04:00 NaN NaN 11.64 86.7"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feature_list = ['date', 'hvac_N', 'hvac_S', 'air_temp_set_1', 'solar_radiation_set_1']\n",
"extended_energy_data = all_data[feature_list]\n",
"extended_energy_data.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"energy_data = pd.read_csv(dataPATH + r\"\\hvac_data_1h.csv\")\n",
"\n",
"# Convert the date column to datetime\n",
"energy_data['date'] = pd.to_datetime(energy_data['date'], format = \"%Y-%m-%d %H:%M:%S\")\n",
"\n",
"energy_data['day_of_week'] = energy_data['date'].dt.weekday\n",
"# Filter the data for the year 2019\n",
"df_filtered = energy_data[ (energy_data.date.dt.date >date(2019, 1, 20)) & (energy_data.date.dt.date< date(2019, 7, 26))]\n",
"\n",
"# Check for NA values in the DataFrame\n",
"if df_filtered.isna().any().any():\n",
" print(\"There are NA values in the DataFrame columns.\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"testdataset_df = df_filtered[(df_filtered.date.dt.date <date(2019, 2, 20))]\n",
"\n",
"traindataset_df = df_filtered[ (df_filtered.date.dt.date >date(2019, 2, 21))]\n",
"\n",
"testdataset = testdataset_df.drop(columns=[\"date\"]).values\n",
"\n",
"traindataset = traindataset_df.drop(columns=[\"date\"]).values\n",
"\n",
"columns_with_na = traindataset_df.columns[traindataset_df.isna().any()].tolist()\n",
"columns_with_na"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"traindataset = traindataset.astype('float32')\n",
"testdataset = testdataset.astype('float32')\n",
"\n",
"mintest = np.min(testdataset)\n",
"maxtest = np.max(testdataset)\n",
"\n",
"scaler = MinMaxScaler(feature_range=(0, 1))\n",
"traindataset = scaler.fit_transform(traindataset)\n",
"testdataset = scaler.transform(testdataset)"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"57/58 [============================>.] - ETA: 0s - loss: 0.0238\n",
"Epoch 1: val_loss improved from inf to 0.01717, saving model to lstm_energy_01.keras\n",
"58/58 [==============================] - 11s 62ms/step - loss: 0.0238 - val_loss: 0.0172\n",
"Epoch 2/5\n",
"56/58 [===========================>..] - ETA: 0s - loss: 0.0139\n",
"Epoch 2: val_loss improved from 0.01717 to 0.01117, saving model to lstm_energy_01.keras\n",
"58/58 [==============================] - 2s 42ms/step - loss: 0.0139 - val_loss: 0.0112\n",
"Epoch 3/5\n",
"57/58 [============================>.] - ETA: 0s - loss: 0.0116\n",
"Epoch 3: val_loss improved from 0.01117 to 0.00990, saving model to lstm_energy_01.keras\n",
"58/58 [==============================] - 2s 36ms/step - loss: 0.0116 - val_loss: 0.0099\n",
"Epoch 4/5\n",
"57/58 [============================>.] - ETA: 0s - loss: 0.0084\n",
"Epoch 4: val_loss improved from 0.00990 to 0.00889, saving model to lstm_energy_01.keras\n",
"58/58 [==============================] - 2s 40ms/step - loss: 0.0084 - val_loss: 0.0089\n",
"Epoch 5/5\n",
"57/58 [============================>.] - ETA: 0s - loss: 0.0066\n",
"Epoch 5: val_loss did not improve from 0.00889\n",
"58/58 [==============================] - 2s 37ms/step - loss: 0.0066 - val_loss: 0.0103\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x1f4931dc790>"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train,test = traindataset,testdataset\n",
"days_in_past = 20\n",
"time_step = 1\n",
"def create_dataset(dataset,time_step):\n",
" x = [[] for _ in range(3)] \n",
" Y = [[] for _ in range(2)]\n",
" for i in range(time_step * days_in_past, len(dataset) - time_step * days_in_past): # -time_step is to ensure that the Y value has enough values\n",
" for j in range(3):\n",
" x[j].append(dataset[(i-time_step*days_in_past):i, j])\n",
" for j in range(2):\n",
" Y[j].append([dataset[x + i, j] for x in range(0,time_step)]) \n",
" x = [np.array(feature_list) for feature_list in x]\n",
" Y = [np.array(feature_list) for feature_list in Y] \n",
" Y = np.stack(Y,axis=1)\n",
" Y = np.reshape(Y, (Y.shape[0], time_step*2))\n",
" return np.stack(x,axis=2), Y\n",
"\n",
"\n",
"X_train, y_train = create_dataset(train, time_step)\n",
"X_test, y_test = create_dataset(test, time_step)\n",
"\n",
"\n",
"model = Sequential()\n",
"model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
"model.add(LSTM(units=50, return_sequences=True))\n",
"model.add(LSTM(units=30))\n",
"model.add(Dense(units=time_step*2))\n",
"\n",
"model.compile(optimizer='adam', loss='mean_squared_error')\n",
"\n",
"checkpoint_path = \"lstm_energy_01.keras\"\n",
"checkpoint_callback = ModelCheckpoint(filepath=checkpoint_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
"model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=64, verbose=1, callbacks=[checkpoint_callback])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"22/22 [==============================] - 0s 8ms/step - loss: 0.0126\n",
"22/22 [==============================] - 1s 7ms/step\n",
"Loss: 0.01257658563554287\n"
]
}
],
"source": [
"loss = model.evaluate(X_test, y_test)\n",
"test_predict1 = model.predict(X_test)\n",
"print(\"Loss: \", loss)\n",
"# Converting values back to the original scale\n",
"scalerBack = MinMaxScaler(feature_range=(mintest, maxtest))\n",
"test_predict2 = scalerBack.fit_transform(test_predict1)\n",
"y_test1 = scalerBack.fit_transform(y_test)\n"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib qt\n",
"\n",
"# Create a 3x3 grid of subplots\n",
"fig, axes = plt.subplots(3, 3, figsize=(10, 10))\n",
"\n",
"# Loop over the value index\n",
"for i, ax in enumerate(axes.flat):\n",
" # Plot your data or perform any other operations\n",
" ax.plot(y_test1[i,0:time_step], label='Original Testing Data', color='blue')\n",
" ax.plot(test_predict2[i,0:time_step], label='Predicted Testing Data', color='red',alpha=0.8)\n",
" # ax.set_title(f'Plot {i+1}')\n",
" ax.set_title('Testing Data - Predicted vs Actual')\n",
" ax.set_xlabel('Time [hours]')\n",
" ax.set_ylabel('Energy Consumption [kW]') \n",
" ax.legend()\n",
"\n",
"# Adjust the spacing between subplots\n",
"plt.tight_layout()\n",
"\n",
"# Show the plot\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib qt\n",
"index = 100\n",
"\n",
"\n",
"plt.plot(y_test[index,0:24], label='Original Testing Data', color='blue')\n",
"plt.plot(test_predict1[index,0:24], label='Predicted Testing Data', color='red',alpha=0.8)\n",
"\n",
"\n",
"plt.title('Testing Data - Predicted vs Actual')\n",
"plt.xlabel('Time [hours]')\n",
"plt.ylabel('Energy [kW]')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_test[1, 0:24]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "experiments",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
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