Wärmepumpen Klasse für meine WP erzeugt (Achtung Standard)

PV Prognose aufsummieren der Strings eingebaut
2025-12-14 07:46:18 +00:00 · 2024-03-08 14:22:11 +01:00
parent 8e7308ff8a
commit 2c0e355845
4 changed files with 164 additions and 44 deletions
--- a/test.py
+++ b/test.py
@@ -37,7 +37,7 @@ discharge_array = np.full(prediction_hours,1) #np.array([1, 0, 1, 0, 1, 1, 1, 1,
 laden_moeglich = np.full(prediction_hours,1) # np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0])
 #np.full(prediction_hours,1)
 eauto = EAuto(soc=10, capacity = 60000, power_charge = 7000, load_allowed = laden_moeglich)
-min_soc_eauto = 100
+min_soc_eauto = 10
 hohe_strafe = 10.0


@@ -60,8 +60,9 @@ gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
 # PV Forecast
 ###############
 #PVforecast = PVForecast(filepath=os.path.join(r'test_data', r'pvprognose.json'))
-PVforecast = PVForecast(prediction_hours = prediction_hours, url="https://api.akkudoktor.net/forecast?lat=50.8588&lon=7.3747&power=5400&azimuth=-10&tilt=7&powerInvertor=2500&horizont=20,40,30,30&power=4800&azimuth=-90&tilt=7&powerInvertor=2500&horizont=20,40,45,50&power=1480&azimuth=-90&tilt=70&powerInvertor=1120&horizont=60,45,30,70&power=1600&azimuth=5&tilt=60&powerInvertor=1200&horizont=60,45,30,70&past_days=5&cellCoEff=-0.36&inverterEfficiency=0.8&albedo=0.25&timezone=Europe%2FBerlin&hourly=relativehumidity_2m%2Cwindspeed_10m")
+PVforecast = PVForecast(prediction_hours = prediction_hours, url="https://api.akkudoktor.net/forecast?lat=50.8588&lon=7.3747&power=5000&azimuth=-10&tilt=7&powerInvertor=10000&horizont=20,27,22,20&power=4800&azimuth=-90&tilt=7&powerInvertor=10000&horizont=30,30,30,50&power=1400&azimuth=-40&tilt=60&powerInvertor=2000&horizont=60,30,0,30&power=1600&azimuth=5&tilt=45&powerInvertor=1400&horizont=45,25,30,60&past_days=5&cellCoEff=-0.36&inverterEfficiency=0.8&albedo=0.25&timezone=Europe%2FBerlin&hourly=relativehumidity_2m%2Cwindspeed_10m")
 pv_forecast = PVforecast.get_pv_forecast_for_date_range(date_now,date) #get_forecast_for_date(date)
+
 temperature_forecast = PVforecast.get_temperature_for_date_range(date_now,date)


@@ -183,45 +184,46 @@ print("Start Lösung:", start_solution)



-# Werkzeug-Setup
-creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
-creator.create("Individual", list, fitness=creator.FitnessMin)
+# # Werkzeug-Setup
+# creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
+# creator.create("Individual", list, fitness=creator.FitnessMin)

-toolbox = base.Toolbox()
+# toolbox = base.Toolbox()




-toolbox.register("attr_bool", random.randint, 0, 1)
-toolbox.register("attr_float", random.uniform, 0.0, 1.0)
-toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_bool,toolbox.attr_float), n=prediction_hours)
+# toolbox.register("attr_bool", random.randint, 0, 1)
+# toolbox.register("attr_float", random.uniform, 0.0, 1.0)
+# toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_bool,toolbox.attr_float), n=prediction_hours)

-start_individual = toolbox.individual()
-start_individual[:] = start_solution
+# start_individual = toolbox.individual()
+# start_individual[:] = start_solution

-toolbox.register("population", tools.initRepeat, list, toolbox.individual)
+# toolbox.register("population", tools.initRepeat, list, toolbox.individual)

-toolbox.register("evaluate", evaluate)
-toolbox.register("mate", tools.cxTwoPoint)
-toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
-toolbox.register("select", tools.selTournament, tournsize=3)
+# toolbox.register("evaluate", evaluate)
+# toolbox.register("mate", tools.cxTwoPoint)
+# toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
+# toolbox.register("select", tools.selTournament, tournsize=3)

-# Genetischer Algorithmus
-def optimize():
-    population = toolbox.population(n=1000)
-    population[0] = start_individual
-    hof = tools.HallOfFame(1)
+# # Genetischer Algorithmus
+# def optimize():
+    # population = toolbox.population(n=1000)
+    # population[0] = start_individual
+    # hof = tools.HallOfFame(1)
    
-    stats = tools.Statistics(lambda ind: ind.fitness.values)
-    stats.register("avg", np.mean)
-    stats.register("min", np.min)
-    stats.register("max", np.max)
+    # stats = tools.Statistics(lambda ind: ind.fitness.values)
+    # stats.register("avg", np.mean)
+    # stats.register("min", np.min)
+    # stats.register("max", np.max)
    
-    algorithms.eaMuPlusLambda(population, toolbox, 100, 200, cxpb=0.5, mutpb=0.2, ngen=1000,   stats=stats, halloffame=hof, verbose=True)
-    #algorithms.eaSimple(population, toolbox, cxpb=0.2, mutpb=0.2, ngen=1000,             stats=stats, halloffame=hof, verbose=True)
-    return hof[0]
+    # algorithms.eaMuPlusLambda(population, toolbox, 100, 200, cxpb=0.5, mutpb=0.2, ngen=1000,   stats=stats, halloffame=hof, verbose=True)
+    # #algorithms.eaSimple(population, toolbox, cxpb=0.2, mutpb=0.2, ngen=1000,             stats=stats, halloffame=hof, verbose=True)
+    # return hof[0]

-best_solution = optimize()
+# best_solution = optimize()
+best_solution = start_solution
 print("Beste Lösung:", best_solution)

 #ems.set_akku_discharge_hours(best_solution)
@@ -232,7 +234,60 @@ print(soc_eauto)
 pprint(o)
 pprint(eauto.get_stuendlicher_soc())

-visualisiere_ergebnisse(gesamtlast,leistung_haushalt,leistung_wp, pv_forecast, specific_date_prices, o,soc_eauto,best_solution[0::2],best_solution[1::2] )
+
+
+
+
+# # Werkzeug-Setup
+# creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
+# creator.create("Individual", list, fitness=creator.FitnessMin)
+
+# toolbox = base.Toolbox()
+
+
+
+
+# toolbox.register("attr_bool", random.randint, 0, 1)
+# toolbox.register("attr_float", random.uniform, 0.0, 1.0)
+# toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_bool,toolbox.attr_float), n=prediction_hours)
+
+# start_individual = toolbox.individual()
+# start_individual[:] = start_solution
+
+# toolbox.register("population", tools.initRepeat, list, toolbox.individual)
+
+# toolbox.register("evaluate", evaluate)
+# toolbox.register("mate", tools.cxTwoPoint)
+# toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
+# toolbox.register("select", tools.selTournament, tournsize=3)
+
+# # Genetischer Algorithmus
+# def optimize():
+    # population = toolbox.population(n=1000)
+    # population[0] = start_individual
+    # hof = tools.HallOfFame(1)
+    
+    # stats = tools.Statistics(lambda ind: ind.fitness.values)
+    # stats.register("avg", np.mean)
+    # stats.register("min", np.min)
+    # stats.register("max", np.max)
+    
+    # algorithms.eaMuPlusLambda(population, toolbox, 100, 200, cxpb=0.5, mutpb=0.2, ngen=1000,   stats=stats, halloffame=hof, verbose=True)
+    # #algorithms.eaSimple(population, toolbox, cxpb=0.2, mutpb=0.2, ngen=1000,             stats=stats, halloffame=hof, verbose=True)
+    # return hof[0]
+
+# best_solution = optimize()
+# print("Beste Lösung:", best_solution)
+
+# #ems.set_akku_discharge_hours(best_solution)
+# o,eauto = evaluate_inner(best_solution)
+
+# soc_eauto = eauto.get_stuendlicher_soc()
+# print(soc_eauto)
+# pprint(o)
+# pprint(eauto.get_stuendlicher_soc())
+
+visualisiere_ergebnisse(gesamtlast,leistung_haushalt,leistung_wp, pv_forecast, specific_date_prices, o,soc_eauto,best_solution[0::2],best_solution[1::2] , temperature_forecast)


 # for data in forecast.get_forecast_data():