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Lastprediction als Service verfügbar und ohne DB Abfrage.
y
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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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