Neues Lastprognose Modell mit Korrektur

flask_server.py

@@ -11,6 +11,7 @@ from modules.class_sommerzeit import *
 from modules.class_soc_calc import *
 from modules.visualize import *
 from modules.class_battery_soc_predictor import *
+from modules.class_load_corrector import *
 import os
 from flask import Flask, send_from_directory
 from pprint import pprint
@@ -18,7 +19,7 @@ import matplotlib
 matplotlib.use('Agg')  # Setzt das Backend auf Agg
 import matplotlib.pyplot as plt
 import string
-from datetime import datetime
+from datetime import datetime, timedelta
 from deap import base, creator, tools, algorithms
 from modules.class_optimize import *
 import numpy as np
@@ -32,6 +33,32 @@ opt_class = optimization_problem(prediction_hours=48, strafe=10)
 
 
 
+@app.route('/last_correction', methods=['GET'])
+def flask_last_correction():
+    if request.method == 'GET':
+        year_energy = float(request.args.get("year_energy"))
+        date_now,date = get_start_enddate(prediction_hours,startdate=datetime.now().date())
+        ###############
+        # Load Forecast
+        ###############
+        lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
+        #leistung_haushalt = lf.get_daily_stats(date)[0,...]  # Datum anpassen
+        leistung_haushalt = lf.get_stats_for_date_range(date_now,date)[0] # Nur Erwartungswert!        
+        
+        gesamtlast = Gesamtlast(prediction_hours=prediction_hours)        
+        gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
+
+        # ###############
+        # # WP
+        # ##############
+        # leistung_wp = wp.simulate_24h(temperature_forecast)
+        # gesamtlast.hinzufuegen("Heatpump", leistung_wp)
+                
+        last = gesamtlast.gesamtlast_berechnen()
+        print(last)
+        #print(specific_date_prices)
+        return jsonify(last.tolist())
+
 
 @app.route('/soc', methods=['GET'])
 def flask_soc():
@@ -65,6 +92,9 @@ def flask_soc():
     return jsonify("Done")
 
 
+
+
+
 @app.route('/strompreis', methods=['GET'])
 def flask_strompreis():
         date_now,date = get_start_enddate(prediction_hours,startdate=datetime.now().date())
@@ -80,14 +110,50 @@ def flask_strompreis():
 def flask_gesamtlast():
     if request.method == 'GET':
         year_energy = float(request.args.get("year_energy"))
-        date_now,date = get_start_enddate(prediction_hours,startdate=datetime.now().date())
+        prediction_hours = int(request.args.get("hours", 48))  # Default to 24 hours if not specified
+        date_now = datetime.now()
+        end_date = (date_now + timedelta(hours=prediction_hours)).strftime('%Y-%m-%d %H:%M:%S')
+
         ###############
         # Load Forecast
         ###############
+        # Instantiate LastEstimator and get measured data
+        estimator = LastEstimator()
+        start_date = (date_now - timedelta(days=60)).strftime('%Y-%m-%d')  # Example: last 60 days
+        end_date = date_now.strftime('%Y-%m-%d')  # Current date
+
+        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')
+        cleaned_data = selected_columns.dropna()
+
+        # Instantiate LoadForecast
         lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
-        #leistung_haushalt = lf.get_daily_stats(date)[0,...]  # Datum anpassen
+
-        leistung_haushalt = lf.get_stats_for_date_range(date_now,date)[0] # Nur Erwartungswert!        
+        # Generate forecast data
-        
+        forecast_list = []
+        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]
+            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)
+
+        forecast_df = pd.concat(forecast_list, ignore_index=True)
+
+        # Create LoadPredictionAdjuster instance
+        adjuster = LoadPredictionAdjuster(cleaned_data, forecast_df, lf)
+        adjuster.calculate_weighted_mean()
+        adjuster.adjust_predictions()
+
+        # Predict the next hours
+        future_predictions = adjuster.predict_next_hours(prediction_hours)
+
+        leistung_haushalt = future_predictions['Adjusted Pred'].values
+
         gesamtlast = Gesamtlast(prediction_hours=prediction_hours)        
         gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
 
@@ -99,9 +165,35 @@ def flask_gesamtlast():
                 
         last = gesamtlast.gesamtlast_berechnen()
         print(last)
-        #print(specific_date_prices)
         return jsonify(last.tolist())
 
+
+# # @app.route('/gesamtlast', methods=['GET'])
+# # def flask_gesamtlast():
+    # # if request.method == 'GET':
+        # # year_energy = float(request.args.get("year_energy"))
+        # # date_now,date = get_start_enddate(prediction_hours,startdate=datetime.now().date())
+        # # ###############
+        # # # Load Forecast
+        # # ###############
+        # # lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
+        # # #leistung_haushalt = lf.get_daily_stats(date)[0,...]  # Datum anpassen
+        # # leistung_haushalt = lf.get_stats_for_date_range(date_now,date)[0] # Nur Erwartungswert!        
+        
+        # # gesamtlast = Gesamtlast(prediction_hours=prediction_hours)        
+        # # gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
+
+        # # # ###############
+        # # # # WP
+        # # # ##############
+        # # # leistung_wp = wp.simulate_24h(temperature_forecast)
+        # # # gesamtlast.hinzufuegen("Heatpump", leistung_wp)
+                
+        # # last = gesamtlast.gesamtlast_berechnen()
+        # # print(last)
+        # # #print(specific_date_prices)
+        # # return jsonify(last.tolist())
+
 @app.route('/pvforecast', methods=['GET'])
 def flask_pvprognose():
     if request.method == 'GET':