mirror of
https://github.com/Akkudoktor-EOS/EOS.git
synced 2025-08-25 06:52:23 +00:00
Lastprediction als Service verfügbar und ohne DB Abfrage.
y
This commit is contained in:
Bla Bla
@@ -44,11 +44,31 @@ class LoadPredictionAdjuster:
|
||||
|
||||
|
||||
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)
|
||||
@@ -124,112 +144,65 @@ class LoadPredictionAdjuster:
|
||||
|
||||
|
||||
|
||||
class LastEstimator:
|
||||
def __init__(self):
|
||||
self.conn_params = db_config
|
||||
self.conn = mariadb.connect(**self.conn_params)
|
||||
|
||||
def fetch_data(self, start_date, end_date):
|
||||
queries = {
|
||||
"Stromzaehler": f"SELECT DATE_FORMAT(timestamp, '%Y-%m-%d %H:00:00') as timestamp, AVG(data) AS Stromzaehler FROM sensor_stromzaehler WHERE topic = 'stromzaehler leistung' AND timestamp BETWEEN '{start_date}' AND '{end_date}' GROUP BY 1 ORDER BY timestamp ASC",
|
||||
"PV": f"SELECT DATE_FORMAT(timestamp, '%Y-%m-%d %H:00:00') as timestamp, AVG(data) AS PV FROM data WHERE topic = 'solarallpower' AND timestamp BETWEEN '{start_date}' AND '{end_date}' GROUP BY 1 ORDER BY timestamp ASC",
|
||||
"Batterie_Strom_PIP": f"SELECT DATE_FORMAT(timestamp, '%Y-%m-%d %H:00:00') as timestamp, AVG(data) AS Batterie_Strom_PIP FROM pip WHERE topic = 'battery_current' AND timestamp BETWEEN '{start_date}' AND '{end_date}' GROUP BY 1 ORDER BY timestamp ASC",
|
||||
"Batterie_Volt_PIP": f"SELECT DATE_FORMAT(timestamp, '%Y-%m-%d %H:00:00') as timestamp, AVG(data) AS Batterie_Volt_PIP FROM pip WHERE topic = 'battery_voltage' AND timestamp BETWEEN '{start_date}' AND '{end_date}' GROUP BY 1 ORDER BY timestamp ASC",
|
||||
"Stromzaehler_Raus": f"SELECT DATE_FORMAT(timestamp, '%Y-%m-%d %H:00:00') as timestamp, AVG(data) AS Stromzaehler_Raus FROM sensor_stromzaehler WHERE topic = 'stromzaehler leistung raus' AND timestamp BETWEEN '{start_date}' AND '{end_date}' GROUP BY 1 ORDER BY timestamp ASC",
|
||||
"Wallbox": f"SELECT DATE_FORMAT(timestamp, '%Y-%m-%d %H:00:00') as timestamp, AVG(data) AS Wallbox_Leistung FROM wallbox WHERE topic = 'power_total' AND timestamp BETWEEN '{start_date}' AND '{end_date}' GROUP BY 1 ORDER BY timestamp ASC",
|
||||
|
||||
}
|
||||
|
||||
|
||||
dataframes = {}
|
||||
for key, query in queries.items():
|
||||
dataframes[key] = pd.read_sql(query, self.conn)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# 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
|
||||
|
||||
return dataframes
|
||||
# lf = LoadForecast(filepath=r'.\load_profiles.npz', year_energy=6000*1000)
|
||||
|
||||
def calculate_last(self, dataframes):
|
||||
# Batterie_Leistung = Batterie_Strom_PIP * Batterie_Volt_PIP
|
||||
dataframes["Batterie_Leistung"] = dataframes["Batterie_Strom_PIP"].merge(dataframes["Batterie_Volt_PIP"], on="timestamp", how="outer")
|
||||
dataframes["Batterie_Leistung"]["Batterie_Leistung"] = dataframes["Batterie_Leistung"]["Batterie_Strom_PIP"] * dataframes["Batterie_Leistung"]["Batterie_Volt_PIP"]
|
||||
# # Initialize an empty DataFrame to hold the forecast data
|
||||
# forecast_list = []
|
||||
|
||||
# Stromzaehler_Saldo = Stromzaehler - Stromzaehler_Raus
|
||||
dataframes["Stromzaehler_Saldo"] = dataframes["Stromzaehler"].merge(dataframes["Stromzaehler_Raus"], on="timestamp", how="outer")
|
||||
dataframes["Stromzaehler_Saldo"]["Stromzaehler_Saldo"] = dataframes["Stromzaehler_Saldo"]["Stromzaehler"] - dataframes["Stromzaehler_Saldo"]["Stromzaehler_Raus"]
|
||||
# # 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)
|
||||
|
||||
# Stromzaehler_Saldo - Batterie_Leistung
|
||||
dataframes["Netzleistung"] = dataframes["Stromzaehler_Saldo"].merge(dataframes["Batterie_Leistung"], on="timestamp", how="outer")
|
||||
dataframes["Netzleistung"]["Netzleistung"] = dataframes["Netzleistung"]["Stromzaehler_Saldo"] - dataframes["Netzleistung"]["Batterie_Leistung"]
|
||||
# # Concatenate all daily forecasts into a single DataFrame
|
||||
# forecast_df = pd.concat(forecast_list, ignore_index=True)
|
||||
|
||||
# Füge die Wallbox-Leistung hinzu
|
||||
dataframes["Netzleistung"] = dataframes["Netzleistung"].merge(dataframes["Wallbox"], on="timestamp", how="left")
|
||||
dataframes["Netzleistung"]["Wallbox_Leistung"] = dataframes["Netzleistung"]["Wallbox_Leistung"].fillna(0) # Fülle fehlende Werte mit 0
|
||||
# # Create an instance of the LoadPredictionAdjuster class
|
||||
# adjuster = LoadPredictionAdjuster(cleaned_data, forecast_df, lf)
|
||||
|
||||
# Last = Netzleistung + PV
|
||||
# Berechne die endgültige Last
|
||||
dataframes["Last"] = dataframes["Netzleistung"].merge(dataframes["PV"], on="timestamp", how="outer")
|
||||
dataframes["Last"]["Last_ohneWallbox"] = dataframes["Last"]["Netzleistung"] + dataframes["Last"]["PV"]
|
||||
dataframes["Last"]["Last"] = dataframes["Last"]["Netzleistung"] + dataframes["Last"]["PV"] - dataframes["Last"]["Wallbox_Leistung"]
|
||||
return dataframes["Last"].dropna()
|
||||
# # Calculate the weighted mean differences
|
||||
# adjuster.calculate_weighted_mean()
|
||||
|
||||
def get_last(self, start_date, end_date):
|
||||
dataframes = self.fetch_data(start_date, end_date)
|
||||
last_df = self.calculate_last(dataframes)
|
||||
return last_df
|
||||
# # Adjust the predictions
|
||||
# adjuster.adjust_predictions()
|
||||
|
||||
# # Plot the results
|
||||
# adjuster.plot_results()
|
||||
|
||||
# # Evaluate the model
|
||||
# adjuster.evaluate_model()
|
||||
|
||||
|
||||
|
||||
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)
|
||||
# # Predict the next x hours
|
||||
# future_predictions = adjuster.predict_next_hours(48)
|
||||
# print(future_predictions)
|
||||
Reference in New Issue
Block a user