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208 lines
9.0 KiB
Python
208 lines
9.0 KiB
Python
import json,sys, os
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from datetime import datetime, timedelta, timezone
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import numpy as np
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from pprint import pprint
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import pandas as pd
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import matplotlib.pyplot as plt
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# from sklearn.model_selection import train_test_split, GridSearchCV
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# from sklearn.ensemble import GradientBoostingRegressor
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# from xgboost import XGBRegressor
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# from statsmodels.tsa.statespace.sarimax import SARIMAX
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# from tensorflow.keras.models import Sequential
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# from tensorflow.keras.layers import Dense, LSTM
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# from tensorflow.keras.optimizers import Adam
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# from sklearn.preprocessing import MinMaxScaler
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# from sklearn.metrics import mean_squared_error, r2_score
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import mariadb
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# from sqlalchemy import create_engine
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.metrics import mean_squared_error, r2_score
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# Fügen Sie den übergeordneten Pfad zum sys.path hinzu
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from config import *
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from modules.class_load import *
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class LoadPredictionAdjuster:
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def __init__(self, measured_data, predicted_data, load_forecast):
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self.measured_data = measured_data
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self.predicted_data = predicted_data
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self.load_forecast = load_forecast
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self.merged_data = self._merge_data()
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self.train_data = None
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self.test_data = None
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self.weekday_diff = None
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self.weekend_diff = None
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def _remove_outliers(self, data, threshold=2):
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# Berechne den Z-Score der 'Last'-Daten
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data['Z-Score'] = np.abs((data['Last'] - data['Last'].mean()) / data['Last'].std())
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# Filtere die Daten nach dem Schwellenwert
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filtered_data = data[data['Z-Score'] < threshold]
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return filtered_data.drop(columns=['Z-Score'])
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def _merge_data(self):
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# Konvertiere die Zeitspalte in beiden Datenrahmen zu datetime
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self.predicted_data['time'] = pd.to_datetime(self.predicted_data['time'])
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self.measured_data['time'] = pd.to_datetime(self.measured_data['time'])
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# Stelle sicher, dass beide Zeitspalten dieselbe Zeitzone haben
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# Measured Data: Setze die Zeitzone auf UTC, falls es tz-naiv ist
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if self.measured_data['time'].dt.tz is None:
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self.measured_data['time'] = self.measured_data['time'].dt.tz_localize('UTC')
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# Predicted Data: Setze ebenfalls UTC und konvertiere anschließend in die lokale Zeitzone
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self.predicted_data['time'] = self.predicted_data['time'].dt.tz_localize('UTC').dt.tz_convert('Europe/Berlin')
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self.measured_data['time'] = self.measured_data['time'].dt.tz_convert('Europe/Berlin')
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# Optional: Entferne die Zeitzoneninformation, wenn du nur lokal arbeiten möchtest
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self.predicted_data['time'] = self.predicted_data['time'].dt.tz_localize(None)
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self.measured_data['time'] = self.measured_data['time'].dt.tz_localize(None)
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# Jetzt kannst du den Merge durchführen
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merged_data = pd.merge(self.measured_data, self.predicted_data, on='time', how='inner')
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print(merged_data)
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merged_data['Hour'] = merged_data['time'].dt.hour
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merged_data['DayOfWeek'] = merged_data['time'].dt.dayofweek
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return merged_data
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def calculate_weighted_mean(self, train_period_weeks=9, test_period_weeks=1):
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self.merged_data = self._remove_outliers(self.merged_data)
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train_end_date = self.merged_data['time'].max() - pd.Timedelta(weeks=test_period_weeks)
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train_start_date = train_end_date - pd.Timedelta(weeks=train_period_weeks)
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test_start_date = train_end_date + pd.Timedelta(hours=1)
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test_end_date = test_start_date + pd.Timedelta(weeks=test_period_weeks) - pd.Timedelta(hours=1)
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self.train_data = self.merged_data[(self.merged_data['time'] >= train_start_date) & (self.merged_data['time'] <= train_end_date)]
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self.test_data = self.merged_data[(self.merged_data['time'] >= test_start_date) & (self.merged_data['time'] <= test_end_date)]
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self.train_data['Difference'] = self.train_data['Last'] - self.train_data['Last Pred']
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weekdays_train_data = self.train_data[self.train_data['DayOfWeek'] < 5]
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weekends_train_data = self.train_data[self.train_data['DayOfWeek'] >= 5]
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self.weekday_diff = weekdays_train_data.groupby('Hour').apply(self._weighted_mean_diff).dropna()
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self.weekend_diff = weekends_train_data.groupby('Hour').apply(self._weighted_mean_diff).dropna()
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def _weighted_mean_diff(self, data):
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train_end_date = self.train_data['time'].max()
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weights = 1 / (train_end_date - data['time']).dt.days.replace(0, np.nan)
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weighted_mean = (data['Difference'] * weights).sum() / weights.sum()
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return weighted_mean
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def adjust_predictions(self):
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self.train_data['Adjusted Pred'] = self.train_data.apply(self._adjust_row, axis=1)
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self.test_data['Adjusted Pred'] = self.test_data.apply(self._adjust_row, axis=1)
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def _adjust_row(self, row):
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if row['DayOfWeek'] < 5:
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return row['Last Pred'] + self.weekday_diff.get(row['Hour'], 0)
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else:
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return row['Last Pred'] + self.weekend_diff.get(row['Hour'], 0)
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def plot_results(self):
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self._plot_data(self.train_data, 'Training')
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self._plot_data(self.test_data, 'Testing')
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def _plot_data(self, data, data_type):
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plt.figure(figsize=(14, 7))
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plt.plot(data['time'], data['Last'], label=f'Actual Last - {data_type}', color='blue')
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plt.plot(data['time'], data['Last Pred'], label=f'Predicted Last - {data_type}', color='red', linestyle='--')
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plt.plot(data['time'], data['Adjusted Pred'], label=f'Adjusted Predicted Last - {data_type}', color='green', linestyle=':')
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plt.xlabel('Time')
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plt.ylabel('Load')
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plt.title(f'Actual vs Predicted vs Adjusted Predicted Load ({data_type} Data)')
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plt.legend()
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plt.grid(True)
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plt.show()
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def evaluate_model(self):
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mse = mean_squared_error(self.test_data['Last'], self.test_data['Adjusted Pred'])
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r2 = r2_score(self.test_data['Last'], self.test_data['Adjusted Pred'])
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print(f'Mean Squared Error: {mse}')
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print(f'R-squared: {r2}')
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def predict_next_hours(self, hours_ahead):
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last_date = self.merged_data['time'].max()
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future_dates = [last_date + pd.Timedelta(hours=i) for i in range(1, hours_ahead + 1)]
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future_df = pd.DataFrame({'time': future_dates})
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future_df['Hour'] = future_df['time'].dt.hour
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future_df['DayOfWeek'] = future_df['time'].dt.dayofweek
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future_df['Last Pred'] = future_df['time'].apply(self._forecast_next_hours)
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future_df['Adjusted Pred'] = future_df.apply(self._adjust_row, axis=1)
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return future_df
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def _forecast_next_hours(self, timestamp):
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date_str = timestamp.strftime('%Y-%m-%d')
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hour = timestamp.hour
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daily_forecast = self.load_forecast.get_daily_stats(date_str)
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return daily_forecast[0][hour] if hour < len(daily_forecast[0]) else np.nan
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# if __name__ == '__main__':
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# estimator = LastEstimator()
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# start_date = "2024-06-01"
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# end_date = "2024-08-01"
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# last_df = estimator.get_last(start_date, end_date)
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# selected_columns = last_df[['timestamp', 'Last']]
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# selected_columns['time'] = pd.to_datetime(selected_columns['timestamp']).dt.floor('H')
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# selected_columns['Last'] = pd.to_numeric(selected_columns['Last'], errors='coerce')
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# # Drop rows with NaN values
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# cleaned_data = selected_columns.dropna()
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# print(cleaned_data)
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# # Create an instance of LoadForecast
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# lf = LoadForecast(filepath=r'.\load_profiles.npz', year_energy=6000*1000)
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# # Initialize an empty DataFrame to hold the forecast data
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# forecast_list = []
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# # Loop through each day in the date range
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# for single_date in pd.date_range(cleaned_data['time'].min().date(), cleaned_data['time'].max().date()):
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# date_str = single_date.strftime('%Y-%m-%d')
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# daily_forecast = lf.get_daily_stats(date_str)
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# mean_values = daily_forecast[0] # Extract the mean values
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# hours = [single_date + pd.Timedelta(hours=i) for i in range(24)]
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# daily_forecast_df = pd.DataFrame({'time': hours, 'Last Pred': mean_values})
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# forecast_list.append(daily_forecast_df)
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# # Concatenate all daily forecasts into a single DataFrame
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# forecast_df = pd.concat(forecast_list, ignore_index=True)
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# # Create an instance of the LoadPredictionAdjuster class
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# adjuster = LoadPredictionAdjuster(cleaned_data, forecast_df, lf)
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# # Calculate the weighted mean differences
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# adjuster.calculate_weighted_mean()
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# # Adjust the predictions
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# adjuster.adjust_predictions()
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# # Plot the results
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# adjuster.plot_results()
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# # Evaluate the model
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# adjuster.evaluate_model()
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# # Predict the next x hours
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# future_predictions = adjuster.predict_next_hours(48)
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# print(future_predictions) |