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]
}
],
"source": [
"import psutil\n",
"import time\n",
"\n",
"def monitor_resources(duration, interval=1):\n",
" # Start- und Endzeit festlegen\n",
" start_time = time.time()\n",
" end_time = start_time + duration\n",
" \n",
" # Listen zur Speicherung der Ressourcendaten\n",
" cpu_usage = []\n",
" memory_usage = []\n",
" \n",
" while time.time() < end_time:\n",
" # CPU und RAM Nutzung ermitteln\n",
" cpu = psutil.cpu_percent(interval=interval)\n",
" memory = psutil.virtual_memory().percent\n",
" \n",
" # Daten speichern\n",
" cpu_usage.append(cpu)\n",
" memory_usage.append(memory)\n",
" \n",
" # Warten bis zum nächsten Intervall\n",
" time.sleep(interval - 0.1)\n",
" \n",
" return cpu_usage, memory_usage\n",
"\n",
"# Nutzung des Monitors für 45 Minuten\n",
"cpu_usage, memory_usage = monitor_resources(duration=2700, interval=1)\n",
"\n",
"# Ergebnisse können dann analysiert oder gespeichert werden\n",
"print(cpu_usage, memory_usage)\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8a3635d0-4846-4403-950d-eec54b49bb00",
"metadata": {},
"outputs": [],
"source": [
"# TBATS for the prediction of energy consumption data over 48 hours"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2dfddc8a-fc2c-46c3-a7a0-9a09133f525c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/sarah/anaconda3/envs/BT2024SARIMAModel/bin/python\n"
]
}
],
"source": [
"import sys\n",
"print(sys.executable)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "84d0b920-74f6-4d2a-8332-6e0253b39b98",
"metadata": {},
"outputs": [],
"source": [
"#Install all required packages with 'conda install NAME' or with pip install NAME'\n",
"# pandas\n",
"# tbats\n",
"# numpy\n",
"# matplotlib\n",
"# scikit-learn"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e0384da2-5323-4348-a574-8882950b41ce",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"#Import all required libraries\n",
"import pandas as pd # Used for data manipulation and analysis\n",
"from pathlib import Path # Used for filesystem path manipulation\n",
"from tbats import TBATS # Used for time series forecasting using TBATS model\n",
"import numpy as np # Used for numerical operations\n",
"from sklearn.metrics import mean_squared_error, mean_absolute_error # Used for calculating error metrics\n",
"import matplotlib.pyplot as plt # Used for creating static, interactive, and animated visualizations\n",
"import matplotlib.dates as mdates # Used for formatting date data on matplotlib plots"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "fbdc50f1-33c0-4820-a3dc-2ebc34c0eb11",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/sarah/anaconda3/envs/BT2024SARIMAModel/bin/python\n"
]
}
],
"source": [
"#To display the current environment\n",
"import sys\n",
"print(sys.executable)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "003b0d56-f8a2-441c-8fd1-0423d71c7088",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First and last rows from dfEnergyAll:\n",
" Lastgang\n",
"Timestamp \n",
"2021-01-01 00:00:00 472.88\n",
"2021-01-01 00:15:00 498.83\n",
"2021-01-01 00:30:00 480.48\n",
"2021-01-01 00:45:00 446.74\n",
"2021-01-01 01:00:00 459.55\n",
" Lastgang\n",
"Timestamp \n",
"2023-12-31 22:45:00 833.17\n",
"2023-12-31 23:00:00 767.89\n",
"2023-12-31 23:15:00 770.04\n",
"2023-12-31 23:30:00 730.45\n",
"2023-12-31 23:45:00 744.21\n",
"Number of rows in dfEnergyAll: 105108\n"
]
}
],
"source": [
"# Step 1 - Reading Data\n",
"\n",
"## dfEnergyAll: All data from the transformer station from 2021-2023\n",
"\n",
"# Path to the directory with the Excel files\n",
"directory_path = Path('/home/sarah/Documents/BT2024/All')\n",
"\n",
"# Create a list of all Excel files in the directory\n",
"file_paths = list(directory_path.glob('*.xlsx'))\n",
"\n",
"# List for storing individual DataFrames\n",
"dfs = []\n",
"\n",
"# Loop over all file paths\n",
"for file_path in file_paths:\n",
" # Read the Excel file\n",
" df = pd.read_excel(file_path)\n",
" \n",
" # Convert the 'Timestamp' column into datetime\n",
" df['Timestamp'] = pd.to_datetime(df['Timestamp'])\n",
" \n",
" # Convert the 'Lastgang' column into a numeric data type, errors are treated as NaN\n",
" df['Lastgang'] = pd.to_numeric(df['Lastgang'], errors='coerce')\n",
" \n",
" # Sort the DataFrame by 'Timestamp'\n",
" df = df.sort_values(by='Timestamp')\n",
" \n",
" # Perform a linear interpolation for 'Lastgang' on the individual DataFrame\n",
" df['Lastgang'] = df['Lastgang'].interpolate(method='linear')\n",
" \n",
" # Add the DataFrame to the list\n",
" dfs.append(df)\n",
"\n",
"# Merge all DataFrames in the list\n",
"dfEnergyAll = pd.concat(dfs).set_index('Timestamp')\n",
"\n",
"# Sum the Lastgang values for identical timestamps\n",
"dfEnergyAll = dfEnergyAll.groupby('Timestamp').sum()\n",
"\n",
"# Check the resulting DataFrame\n",
"print(\"First and last rows from dfEnergyAll:\")\n",
"print(dfEnergyAll.head())\n",
"print(dfEnergyAll.tail())\n",
"\n",
"# Display the number of rows in dfEnergyAll\n",
"print(\"Number of rows in dfEnergyAll:\", dfEnergyAll.shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "4b8a32d6-8997-4a18-b931-353c4786a8a1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Missing timestamps in dfEnergyAll:\n",
"DatetimeIndex(['2021-03-28 02:00:00', '2021-03-28 02:15:00',\n",
" '2021-03-28 02:30:00', '2021-03-28 02:45:00',\n",
" '2022-03-27 02:00:00', '2022-03-27 02:15:00',\n",
" '2022-03-27 02:30:00', '2022-03-27 02:45:00',\n",
" '2023-03-26 02:00:00', '2023-03-26 02:15:00',\n",
" '2023-03-26 02:30:00', '2023-03-26 02:45:00'],\n",
" dtype='datetime64[ns]', name='Timestamp', freq=None)\n",
"Check after adding the missing timestamps:\n",
" Lastgang\n",
"2021-03-28 02:00:00 372.28\n",
"2021-03-28 02:15:00 372.28\n",
"2021-03-28 02:30:00 372.28\n",
"2021-03-28 02:45:00 372.28\n",
"2022-03-27 02:00:00 554.20\n",
"2022-03-27 02:15:00 554.20\n",
"2022-03-27 02:30:00 554.20\n",
"2022-03-27 02:45:00 554.20\n",
"2023-03-26 02:00:00 520.34\n",
"2023-03-26 02:15:00 520.34\n",
"2023-03-26 02:30:00 520.34\n",
"2023-03-26 02:45:00 520.34\n",
"Number of rows in dfEnergyAll: 105120\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_955451/1014544308.py:32: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
" dfEnergyAll = dfEnergyAll.sort_index().fillna(method='ffill')\n"
]
}
],
"source": [
"# Step 2 - Preparing the Data\n",
"\n",
"# Create a complete timestamp index for the years 2021 - 2023 in 15-minute intervals\n",
"all_timestamps = pd.date_range(start='2021-01-01 00:00:00', end='2023-12-31 23:45:00', freq='15T')\n",
"\n",
"# Convert this into a DataFrame\n",
"df_all_timestamps = pd.DataFrame(all_timestamps, columns=['Timestamp'])\n",
"df_all_timestamps = df_all_timestamps.set_index('Timestamp')\n",
"\n",
"# Compare the complete timestamp index with dfEnergyAll\n",
"missing_timestamps = df_all_timestamps.index.difference(dfEnergyAll.index)\n",
"\n",
"print(\"Missing timestamps in dfEnergyAll:\")\n",
"print(missing_timestamps)\n",
"\n",
"\n",
"# Missing timestamps\n",
"missing_timestamps = pd.DatetimeIndex(['2021-03-28 02:00:00', '2021-03-28 02:15:00',\n",
" '2021-03-28 02:30:00', '2021-03-28 02:45:00',\n",
" '2022-03-27 02:00:00', '2022-03-27 02:15:00',\n",
" '2022-03-27 02:30:00', '2022-03-27 02:45:00',\n",
" '2023-03-26 02:00:00', '2023-03-26 02:15:00',\n",
" '2023-03-26 02:30:00', '2023-03-26 02:45:00'])\n",
"\n",
"# Create a DataFrame with the missing timestamps\n",
"df_missing = pd.DataFrame(index=missing_timestamps)\n",
"\n",
"# Merge this DataFrame with the original DataFrame\n",
"dfEnergyAll = dfEnergyAll.combine_first(df_missing)\n",
"\n",
"# Fill in the missing values. Use 'ffill' for forward fill.\n",
"dfEnergyAll = dfEnergyAll.sort_index().fillna(method='ffill')\n",
"\n",
"print(\"Check after adding the missing timestamps:\")\n",
"print(dfEnergyAll.loc[missing_timestamps])\n",
"\n",
"# Number of rows in dfEnergyAll\n",
"print(\"Number of rows in dfEnergyAll:\", dfEnergyAll.shape[0])"
]
},
{
"cell_type": "markdown",
"id": "16b5aaf0-7557-46bb-98c0-3d52b35b2df0",
"metadata": {},
"source": [
"## Performing TBATS with energy consumption data for the years 2021-2023"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "f91675e6-9993-4cab-b28c-bbcc6ef979c7",
"metadata": {},
"outputs": [],
"source": [
"# Step 3 - Creating the TBATS model and generating predictions\n",
"\n",
"# Define training size (All data except the last 48 hours) and test size (48 hours)\n",
"train_size = len(dfEnergyAll) - (2 * 24 * 4) \n",
"test_size = 2 * 24 * 4 \n",
"\n",
"# Split into training and test data\n",
"train_data = dfEnergyAll['Lastgang'][:train_size]\n",
"test_data = dfEnergyAll['Lastgang'][train_size:]\n",
"\n",
"# Model initialization and training\n",
"# 96 as the period for daily seasonality, 672 as the period for weekly seasonality (at 15-minute intervals)\n",
"estimator = TBATS(seasonal_periods=[96, 672], use_arma_errors=False, use_box_cox=False)\n",
"fitted_model = estimator.fit(train_data)\n",
"\n",
"# Create forecasts for the length of the test size (48 hours)\n",
"y_forecast = fitted_model.forecast(steps=test_size)\n",
"\n",
"# Annotate forecast results with timestamps\n",
"last_timestamp = dfEnergyAll.index[train_size - 1]\n",
"future_timestamps = pd.date_range(start=last_timestamp, periods=test_size + 1, freq='15T')[1:]\n",
"forecast_series = pd.Series(y_forecast, index=future_timestamps)\n",
"\n",
"# Annual seasonality not integrated into the model as a period because it already provides good results and could lead to overfitting"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "0be51533-9646-4b7e-84e4-b5c2cde57acc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Akaike Information Criterion (AIC): 2004401.1178947324\n",
"Mean Squared Error (MSE) for the last 2 days: 16455.892322387703\n",
"Mean Absolute Error (MAE) for the last 2 days: 85.23397838663617\n",
"Root Mean Square Error (RMSE) for the last 2 days: 128.2805219914064\n",
"Mean Absolute Percentage Error (MAPE) for the last 2 days: 9.996047807944556%\n"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 1200x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Step 4 - Error metrics and visualization\n",
"\n",
"# Output AIC and BIC\n",
"print(f\"Akaike Information Criterion (AIC): {fitted_model.aic}\")\n",
"\n",
"# Calculate error metrics\n",
"mse = mean_squared_error(test_data, forecast_series)\n",
"mae = mean_absolute_error(test_data, forecast_series)\n",
"rmse = np.sqrt(mse)\n",
"mape = np.mean(np.abs((test_data - forecast_series) / test_data)) * 100\n",
"\n",
"# Output error metrics\n",
"print(f\"Mean Squared Error (MSE) for the last 2 days: {mse}\")\n",
"print(f\"Mean Absolute Error (MAE) for the last 2 days: {mae}\")\n",
"print(f\"Root Mean Square Error (RMSE) for the last 2 days: {rmse}\")\n",
"print(f\"Mean Absolute Percentage Error (MAPE) for the last 2 days: {mape}%\")\n",
"\n",
"# Visualization\n",
"plt.figure(figsize=(12, 6))\n",
"plt.plot(test_data.index, test_data, label='Actual Values', color='#3E7A6F')\n",
"plt.plot(future_timestamps, forecast_series, label='Forecasted Values', color='#7DFFE7')\n",
"plt.legend()\n",
"plt.title('Comparison of Actual and Forecasted Values: TBATS over 48 Hours')\n",
"plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m-%d %H:%M'))\n",
"plt.gca().xaxis.set_major_locator(mdates.HourLocator(interval=3))\n",
"plt.gcf().autofmt_xdate()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "c546aea9-88e3-4f09-acda-ea799ccf3c91",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BIC: 791370.1280251632\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"# Berechnung der Anzahl der Beobachtungen\n",
"n = len(train_data)\n",
"\n",
"# Berechnung der Residuen und der RSS\n",
"# Überprüfen Sie, ob fitted_model.y_hat existiert oder berechnen Sie die Vorhersagen neu\n",
"try:\n",
" y_hat = fitted_model.y_hat\n",
"except AttributeError:\n",
" y_hat = fitted_model.forecast(steps=len(train_data)) # Vorhersage neu berechnen\n",
"\n",
"residuals = train_data - y_hat\n",
"RSS = np.sum(residuals**2)\n",
"\n",
"# Schätzung der Anzahl der Parameter (k)\n",
"k = 2 * 3 * len(estimator.seasonal_periods) # Annehmen 3 Fourier-Terme pro Saisonalität\n",
"\n",
"# Berechnung des BIC\n",
"BIC = n * np.log(RSS / n) + k * np.log(n)\n",
"\n",
"print(f'BIC: {BIC}')"
]
},
{
"cell_type": "markdown",
"id": "1ee06300-992c-4e5d-b038-22f1a9361889",
"metadata": {},
"source": [
"## The same TBATS model, but with 'use_arma_errors=True'"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d3a278b2-d8aa-422f-8c5c-d66e138bdc48",
"metadata": {},
"outputs": [],
"source": [
"# Step 3 - Creating the TBATS model and generating predictions\n",
"\n",
"# Define training size (All data except the last 48 hours) and test size (48 hours)\n",
"train_size = len(dfEnergyAll) - (2 * 24 * 4) \n",
"test_size = 2 * 24 * 4 \n",
"\n",
"# Split into training and test data\n",
"train_data = dfEnergyAll['Lastgang'][:train_size]\n",
"test_data = dfEnergyAll['Lastgang'][train_size:]\n",
"\n",
"# Model initialization and training\n",
"# 96 as the period for daily seasonality, 672 as the period for weekly seasonality (at 15-minute intervals)\n",
"estimator = TBATS(seasonal_periods=[96, 672], use_arma_errors=True, use_box_cox=False)\n",
"fitted_model = estimator.fit(train_data)\n",
"\n",
"# Create forecasts for the length of the test size (48 hours)\n",
"y_forecast = fitted_model.forecast(steps=test_size)\n",
"\n",
"# Annotate forecast results with timestamps\n",
"last_timestamp = dfEnergyAll.index[train_size - 1]\n",
"future_timestamps = pd.date_range(start=last_timestamp, periods=test_size + 1, freq='15T')[1:]\n",
"forecast_series = pd.Series(y_forecast, index=future_timestamps)\n",
"\n",
"# Annual seasonality not integrated into the model as a period because it already provides good results and could lead to overfitting"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fdc96fb8-f11b-4eea-b10b-1a943163d2c2",
"metadata": {},
"outputs": [],
"source": [
"# Step 4 - Error metrics and visualization\n",
"\n",
"# Output AIC and BIC\n",
"print(f\"Akaike Information Criterion (AIC): {fitted_model.aic}\")\n",
"print(f\"Bayesian Information Criterion (BIC): {fitted_model.bic}\")\n",
"\n",
"# Calculate error metrics\n",
"mse = mean_squared_error(test_data, forecast_series)\n",
"mae = mean_absolute_error(test_data, forecast_series)\n",
"rmse = np.sqrt(mse)\n",
"mape = np.mean(np.abs((test_data - forecast_series) / test_data)) * 100\n",
"\n",
"# Output error metrics\n",
"print(f\"Mean Squared Error (MSE) for the last 2 days: {mse}\")\n",
"print(f\"Mean Absolute Error (MAE) for the last 2 days: {mae}\")\n",
"print(f\"Root Mean Square Error (RMSE) for the last 2 days: {rmse}\")\n",
"print(f\"Mean Absolute Percentage Error (MAPE) for the last 2 days: {mape}%\")\n",
"\n",
"# Visualization\n",
"plt.figure(figsize=(12, 6))\n",
"plt.plot(test_data.index, test_data, label='Actual Values', color='#3E7A6F')\n",
"plt.plot(future_timestamps, forecast_series, label='Forecasted Values', color='#7DFFE7')\n",
"plt.legend()\n",
"plt.title('Comparison of Actual and Forecasted Values: TBATS over 48 Hours')\n",
"plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m-%d %H:%M'))\n",
"plt.gca().xaxis.set_major_locator(mdates.HourLocator(interval=3))\n",
"plt.gcf().autofmt_xdate()\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
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