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