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Lastprediction als Service verfügbar und ohne DB Abfrage.
y
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Bla Bla
@@ -7,7 +7,8 @@ strafe=10
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moegliche_ladestroeme_in_prozent = [0.0 ,6.0/16.0, 7.0/16.0, 8.0/16.0, 9.0/16.0, 10.0/16.0, 11.0/16.0, 12.0/16.0, 13.0/16.0, 14.0/16.0, 15.0/16.0, 1.0 ]
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db_config = {
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# Optional
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db_config = {
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'user': '',
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'password': '',
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'host': '192.168.1.135',
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218
flask_server.py
218
flask_server.py
@@ -33,31 +33,31 @@ opt_class = optimization_problem(prediction_hours=prediction_hours, strafe=10, o
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@app.route('/last_correction', methods=['GET'])
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def flask_last_correction():
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if request.method == 'GET':
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year_energy = float(request.args.get("year_energy"))
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date_now,date = get_start_enddate(prediction_hours,startdate=datetime.now().date())
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###############
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# Load Forecast
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###############
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lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
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#leistung_haushalt = lf.get_daily_stats(date)[0,...] # Datum anpassen
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leistung_haushalt = lf.get_stats_for_date_range(date_now,date)[0] # Nur Erwartungswert!
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gesamtlast = Gesamtlast(prediction_hours=prediction_hours)
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gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
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# @app.route('/last_correction', methods=['GET'])
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# def flask_last_correction():
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# if request.method == 'GET':
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# year_energy = float(request.args.get("year_energy"))
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# date_now,date = get_start_enddate(prediction_hours,startdate=datetime.now().date())
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# ###############
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# # WP
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# ##############
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# leistung_wp = wp.simulate_24h(temperature_forecast)
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# gesamtlast.hinzufuegen("Heatpump", leistung_wp)
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# # Load Forecast
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# ###############
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# lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
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# #leistung_haushalt = lf.get_daily_stats(date)[0,...] # Datum anpassen
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# leistung_haushalt = lf.get_stats_for_date_range(date_now,date)[0] # Nur Erwartungswert!
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# gesamtlast = Gesamtlast(prediction_hours=prediction_hours)
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# gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
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# # ###############
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# # # WP
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# # ##############
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# # leistung_wp = wp.simulate_24h(temperature_forecast)
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# # gesamtlast.hinzufuegen("Heatpump", leistung_wp)
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last = gesamtlast.gesamtlast_berechnen()
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print(last)
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#print(specific_date_prices)
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return jsonify(last.tolist())
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# last = gesamtlast.gesamtlast_berechnen()
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# print(last)
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# #print(specific_date_prices)
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# return jsonify(last.tolist())
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@app.route('/soc', methods=['GET'])
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@@ -106,66 +106,146 @@ def flask_strompreis():
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return jsonify(specific_date_prices.tolist())
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@app.route('/gesamtlast', methods=['GET'])
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# Die letzten X gemessenen Daten + gesamtlast Simple oder eine andere Schätung als Input
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# Daraus wird dann eine neuen Lastprognose erstellt welche korrigiert ist.
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# Input:
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@app.route('/gesamtlast', methods=['POST'])
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def flask_gesamtlast():
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if request.method == 'GET':
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year_energy = float(request.args.get("year_energy"))
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prediction_hours = int(request.args.get("hours", 48)) # Default to 24 hours if not specified
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date_now = datetime.now()
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end_date = (date_now + timedelta(hours=prediction_hours)).strftime('%Y-%m-%d %H:%M:%S')
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# Daten aus dem JSON-Body abrufen
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data = request.get_json()
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###############
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# Load Forecast
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###############
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# Instantiate LastEstimator and get measured data
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estimator = LastEstimator()
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start_date = (date_now - timedelta(days=60)).strftime('%Y-%m-%d') # Example: last 60 days
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end_date = date_now.strftime('%Y-%m-%d') # Current date
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# Extract year_energy and prediction_hours from the request JSON
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year_energy = float(data.get("year_energy"))
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prediction_hours = int(data.get("hours", 48)) # Default to 48 hours if not specified
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last_df = estimator.get_last(start_date, end_date)
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# Measured data as JSON
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measured_data_json = data.get("measured_data")
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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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cleaned_data = selected_columns.dropna()
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# Convert JSON data into a Pandas DataFrame
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measured_data = pd.DataFrame(measured_data_json)
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# Make sure the 'time' column is in datetime format
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measured_data['time'] = pd.to_datetime(measured_data['time'])
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# Instantiate LoadForecast
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lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
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# Check if the datetime has timezone info, if not, assume it's local time
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if measured_data['time'].dt.tz is None:
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# Treat it as local time and localize it
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measured_data['time'] = measured_data['time'].dt.tz_localize('Europe/Berlin')
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else:
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# Convert the time to local timezone (e.g., 'Europe/Berlin')
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measured_data['time'] = measured_data['time'].dt.tz_convert('Europe/Berlin')
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# Generate forecast data
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forecast_list = []
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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]
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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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# Remove timezone info after conversion
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measured_data['time'] = measured_data['time'].dt.tz_localize(None)
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# Instantiate LoadForecast and generate forecast data
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lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
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forecast_df = pd.concat(forecast_list, ignore_index=True)
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# Generate forecast data based on the measured data time range
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forecast_list = []
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for single_date in pd.date_range(measured_data['time'].min().date(), measured_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]
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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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# Create LoadPredictionAdjuster instance
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adjuster = LoadPredictionAdjuster(cleaned_data, forecast_df, lf)
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adjuster.calculate_weighted_mean()
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adjuster.adjust_predictions()
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# Concatenate all daily forecasts into a single DataFrame
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predicted_data = pd.concat(forecast_list, ignore_index=True)
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#print(predicted_data)
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# Create LoadPredictionAdjuster instance
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adjuster = LoadPredictionAdjuster(measured_data, predicted_data, lf)
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# Predict the next hours
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future_predictions = adjuster.predict_next_hours(prediction_hours)
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# Calculate weighted mean and adjust predictions
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adjuster.calculate_weighted_mean()
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adjuster.adjust_predictions()
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leistung_haushalt = future_predictions['Adjusted Pred'].values
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# Predict the next x hours
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future_predictions = adjuster.predict_next_hours(prediction_hours)
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gesamtlast = Gesamtlast(prediction_hours=prediction_hours)
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gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
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# Extract the household power predictions
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leistung_haushalt = future_predictions['Adjusted Pred'].values
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# Instantiate Gesamtlast and add household power predictions
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gesamtlast = Gesamtlast(prediction_hours=prediction_hours)
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gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
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# ###############
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# # WP (optional)
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# ###############
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# leistung_wp = wp.simulate_24h(temperature_forecast)
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# gesamtlast.hinzufuegen("Heatpump", leistung_wp)
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# Calculate the total load
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last = gesamtlast.gesamtlast_berechnen()
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# Return the calculated load as JSON
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return jsonify(last.tolist())
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# @app.route('/gesamtlast', methods=['GET'])
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# def flask_gesamtlast():
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# if request.method == 'GET':
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# year_energy = float(request.args.get("year_energy"))
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# prediction_hours = int(request.args.get("hours", 48)) # Default to 24 hours if not specified
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# date_now = datetime.now()
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# end_date = (date_now + timedelta(hours=prediction_hours)).strftime('%Y-%m-%d %H:%M:%S')
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# ###############
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# # WP
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# ##############
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# leistung_wp = wp.simulate_24h(temperature_forecast)
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# gesamtlast.hinzufuegen("Heatpump", leistung_wp)
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# # Load Forecast
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# ###############
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# # Instantiate LastEstimator and get measured data
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# estimator = LastEstimator()
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# start_date = (date_now - timedelta(days=60)).strftime('%Y-%m-%d') # Example: last 60 days
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# end_date = date_now.strftime('%Y-%m-%d') # Current date
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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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# cleaned_data = selected_columns.dropna()
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# # Instantiate LoadForecast
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# lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
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# # Generate forecast data
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# forecast_list = []
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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]
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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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# forecast_df = pd.concat(forecast_list, ignore_index=True)
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# # Create LoadPredictionAdjuster instance
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# adjuster = LoadPredictionAdjuster(cleaned_data, forecast_df, lf)
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# adjuster.calculate_weighted_mean()
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# adjuster.adjust_predictions()
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# # Predict the next hours
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# future_predictions = adjuster.predict_next_hours(prediction_hours)
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# leistung_haushalt = future_predictions['Adjusted Pred'].values
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# gesamtlast = Gesamtlast(prediction_hours=prediction_hours)
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# gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
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# # ###############
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# # # WP
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# # ##############
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# # leistung_wp = wp.simulate_24h(temperature_forecast)
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# # gesamtlast.hinzufuegen("Heatpump", leistung_wp)
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last = gesamtlast.gesamtlast_berechnen()
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print(last)
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return jsonify(last.tolist())
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# last = gesamtlast.gesamtlast_berechnen()
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# print(last)
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# return jsonify(last.tolist())
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@app.route('/gesamtlast_simple', methods=['GET'])
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@@ -44,11 +44,31 @@ class LoadPredictionAdjuster:
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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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@@ -124,112 +144,65 @@ class LoadPredictionAdjuster:
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class LastEstimator:
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def __init__(self):
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self.conn_params = db_config
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self.conn = mariadb.connect(**self.conn_params)
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def fetch_data(self, start_date, end_date):
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queries = {
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"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",
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"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",
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"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",
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"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",
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"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",
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"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",
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}
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dataframes = {}
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for key, query in queries.items():
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dataframes[key] = pd.read_sql(query, self.conn)
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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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return dataframes
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# lf = LoadForecast(filepath=r'.\load_profiles.npz', year_energy=6000*1000)
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def calculate_last(self, dataframes):
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# Batterie_Leistung = Batterie_Strom_PIP * Batterie_Volt_PIP
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dataframes["Batterie_Leistung"] = dataframes["Batterie_Strom_PIP"].merge(dataframes["Batterie_Volt_PIP"], on="timestamp", how="outer")
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dataframes["Batterie_Leistung"]["Batterie_Leistung"] = dataframes["Batterie_Leistung"]["Batterie_Strom_PIP"] * dataframes["Batterie_Leistung"]["Batterie_Volt_PIP"]
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# # Initialize an empty DataFrame to hold the forecast data
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# forecast_list = []
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# Stromzaehler_Saldo = Stromzaehler - Stromzaehler_Raus
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dataframes["Stromzaehler_Saldo"] = dataframes["Stromzaehler"].merge(dataframes["Stromzaehler_Raus"], on="timestamp", how="outer")
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dataframes["Stromzaehler_Saldo"]["Stromzaehler_Saldo"] = dataframes["Stromzaehler_Saldo"]["Stromzaehler"] - dataframes["Stromzaehler_Saldo"]["Stromzaehler_Raus"]
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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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# 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)
|
||||
@@ -8,6 +8,5 @@ deap
|
||||
scipy
|
||||
scikit-learn
|
||||
pandas
|
||||
tensorflow
|
||||
joblib1.4.0
|
||||
mariadb
|
||||
Reference in New Issue
Block a user