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Wärmepumpen Klasse für meine WP erzeugt (Achtung Standard)
PV Prognose aufsummieren der Strings eingebaut
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@ -3,7 +3,7 @@ from datetime import datetime, timedelta, timezone
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import numpy as np
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from pprint import pprint
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# Lade die .npz-Datei beim Start der Anwendung
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class Waermepumpe:
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def __init__(self, max_heizleistung, prediction_hours):
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self.max_heizleistung = max_heizleistung
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@ -21,9 +21,11 @@ class Waermepumpe:
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return heizleistung
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def elektrische_leistung_berechnen(self, aussentemperatur):
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heizleistung = self.heizleistung_berechnen(aussentemperatur)
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cop = self.cop_berechnen(aussentemperatur)
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return heizleistung / cop
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#heizleistung = self.heizleistung_berechnen(aussentemperatur)
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#cop = self.cop_berechnen(aussentemperatur)
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return 1164 -77.8*aussentemperatur + 1.62*aussentemperatur**2.0
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#1253.0*np.math.pow(aussentemperatur,-0.0682)
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def simulate_24h(self, temperaturen):
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leistungsdaten = []
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@ -37,6 +39,40 @@ class Waermepumpe:
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leistungsdaten.append(elektrische_leistung)
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return leistungsdaten
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# # Lade die .npz-Datei beim Start der Anwendung
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# class Waermepumpe:
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# def __init__(self, max_heizleistung, prediction_hours):
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# self.max_heizleistung = max_heizleistung
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# self.prediction_hours = prediction_hours
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# def cop_berechnen(self, aussentemperatur):
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# cop = 3.0 + (aussentemperatur-0) * 0.1
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# return max(cop, 1)
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# def heizleistung_berechnen(self, aussentemperatur):
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# #235.092 kWh + Temperatur * -11.645
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# heizleistung = (((235.0) + aussentemperatur*(-11.645))*1000)/24.0
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# heizleistung = min(self.max_heizleistung,heizleistung)
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# return heizleistung
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# def elektrische_leistung_berechnen(self, aussentemperatur):
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# heizleistung = self.heizleistung_berechnen(aussentemperatur)
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# cop = self.cop_berechnen(aussentemperatur)
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# return heizleistung / cop
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# def simulate_24h(self, temperaturen):
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# leistungsdaten = []
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# # Überprüfen, ob das Temperaturarray die richtige Größe hat
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# if len(temperaturen) != self.prediction_hours:
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# raise ValueError("Das Temperaturarray muss genau "+str(self.prediction_hours)+" Einträge enthalten, einen für jede Stunde des Tages.")
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# for temp in temperaturen:
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# elektrische_leistung = self.elektrische_leistung_berechnen(temp)
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# leistungsdaten.append(elektrische_leistung)
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# return leistungsdaten
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@ -52,17 +52,36 @@ class PVForecast:
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def process_data(self, data):
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self.meta = data.get('meta', {})
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values = data.get('values', [])[0]
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for value in values:
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all_values = data.get('values', [])
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# Berechnung der Summe der DC- und AC-Leistungen für jeden Zeitstempel
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for i in range(len(all_values[0])): # Annahme, dass alle Listen gleich lang sind
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sum_dc_power = sum(values[i]['dcPower'] for values in all_values)
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sum_ac_power = sum(values[i]['power'] for values in all_values)
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# Erstellen eines ForecastData-Objekts mit den summierten Werten
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forecast = ForecastData(
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date_time=value.get('datetime'),
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dc_power=value.get('dcPower'),
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ac_power=value.get('power'),
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windspeed_10m=value.get('windspeed_10m'),
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temperature=value.get('temperature')
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date_time=all_values[0][i].get('datetime'),
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dc_power=sum_dc_power,
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ac_power=sum_ac_power,
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# Optional: Weitere Werte wie Windspeed und Temperature, falls benötigt
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windspeed_10m=all_values[0][i].get('windspeed_10m'),
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temperature=all_values[0][i].get('temperature')
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)
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self.forecast_data.append(forecast)
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# values = data.get('values', [])[0]
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# for value in values:
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# forecast = ForecastData(
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# date_time=value.get('datetime'),
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# dc_power=value.get('dcPower'),
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# ac_power=value.get('power'),
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# windspeed_10m=value.get('windspeed_10m'),
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# temperature=value.get('temperature')
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# )
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# self.forecast_data.append(forecast)
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def load_data_from_file(self, filepath):
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with open(filepath, 'r') as file:
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data = json.load(file)
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@ -3,7 +3,7 @@ from modules.class_load_container import Gesamtlast # Stellen Sie sicher, dass
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import matplotlib.pyplot as plt
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def visualisiere_ergebnisse(gesamtlast,leistung_haushalt,leistung_wp, pv_forecast, strompreise, ergebnisse, soc_eauto, discharge_hours, laden_moeglich):
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def visualisiere_ergebnisse(gesamtlast,leistung_haushalt,leistung_wp, pv_forecast, strompreise, ergebnisse, soc_eauto, discharge_hours, laden_moeglich, temperature):
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# Last und PV-Erzeugung
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plt.figure(figsize=(14, 10))
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@ -70,6 +70,16 @@ def visualisiere_ergebnisse(gesamtlast,leistung_haushalt,leistung_wp, pv_forecas
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ax1.axvspan(hour, hour+1, color='green',ymax=value, alpha=0.3, label='Lademöglichkeit' if hour == 0 else "")
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ax1.legend(loc='upper left')
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ax1 = plt.subplot(3, 2, 6)
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ax1.plot(stunden, temperature, label='Temperatur °C', marker='x')
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ax2 = ax1.twinx()
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ax2.plot(stunden, leistung_wp, label='Wärmepumpe W', marker='x')
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plt.legend(loc='upper left')
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115
test.py
115
test.py
@ -37,7 +37,7 @@ discharge_array = np.full(prediction_hours,1) #np.array([1, 0, 1, 0, 1, 1, 1, 1,
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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])
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#np.full(prediction_hours,1)
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eauto = EAuto(soc=10, capacity = 60000, power_charge = 7000, load_allowed = laden_moeglich)
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min_soc_eauto = 100
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min_soc_eauto = 10
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hohe_strafe = 10.0
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@ -60,8 +60,9 @@ gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
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# PV Forecast
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###############
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#PVforecast = PVForecast(filepath=os.path.join(r'test_data', r'pvprognose.json'))
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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")
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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")
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pv_forecast = PVforecast.get_pv_forecast_for_date_range(date_now,date) #get_forecast_for_date(date)
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temperature_forecast = PVforecast.get_temperature_for_date_range(date_now,date)
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@ -183,45 +184,46 @@ print("Start Lösung:", start_solution)
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# Werkzeug-Setup
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creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
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creator.create("Individual", list, fitness=creator.FitnessMin)
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# # Werkzeug-Setup
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# creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
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# creator.create("Individual", list, fitness=creator.FitnessMin)
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toolbox = base.Toolbox()
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# toolbox = base.Toolbox()
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toolbox.register("attr_bool", random.randint, 0, 1)
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toolbox.register("attr_float", random.uniform, 0.0, 1.0)
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toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_bool,toolbox.attr_float), n=prediction_hours)
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# toolbox.register("attr_bool", random.randint, 0, 1)
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# toolbox.register("attr_float", random.uniform, 0.0, 1.0)
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# toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_bool,toolbox.attr_float), n=prediction_hours)
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start_individual = toolbox.individual()
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start_individual[:] = start_solution
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# start_individual = toolbox.individual()
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# start_individual[:] = start_solution
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toolbox.register("population", tools.initRepeat, list, toolbox.individual)
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# toolbox.register("population", tools.initRepeat, list, toolbox.individual)
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toolbox.register("evaluate", evaluate)
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toolbox.register("mate", tools.cxTwoPoint)
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toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
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toolbox.register("select", tools.selTournament, tournsize=3)
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# toolbox.register("evaluate", evaluate)
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# toolbox.register("mate", tools.cxTwoPoint)
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# toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
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# toolbox.register("select", tools.selTournament, tournsize=3)
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# Genetischer Algorithmus
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def optimize():
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population = toolbox.population(n=1000)
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population[0] = start_individual
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hof = tools.HallOfFame(1)
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# # Genetischer Algorithmus
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# def optimize():
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# population = toolbox.population(n=1000)
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# population[0] = start_individual
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# hof = tools.HallOfFame(1)
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stats = tools.Statistics(lambda ind: ind.fitness.values)
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stats.register("avg", np.mean)
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stats.register("min", np.min)
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stats.register("max", np.max)
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# stats = tools.Statistics(lambda ind: ind.fitness.values)
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# stats.register("avg", np.mean)
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# stats.register("min", np.min)
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# stats.register("max", np.max)
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algorithms.eaMuPlusLambda(population, toolbox, 100, 200, cxpb=0.5, mutpb=0.2, ngen=1000, stats=stats, halloffame=hof, verbose=True)
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#algorithms.eaSimple(population, toolbox, cxpb=0.2, mutpb=0.2, ngen=1000, stats=stats, halloffame=hof, verbose=True)
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return hof[0]
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# algorithms.eaMuPlusLambda(population, toolbox, 100, 200, cxpb=0.5, mutpb=0.2, ngen=1000, stats=stats, halloffame=hof, verbose=True)
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# #algorithms.eaSimple(population, toolbox, cxpb=0.2, mutpb=0.2, ngen=1000, stats=stats, halloffame=hof, verbose=True)
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# return hof[0]
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best_solution = optimize()
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# best_solution = optimize()
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best_solution = start_solution
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print("Beste Lösung:", best_solution)
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#ems.set_akku_discharge_hours(best_solution)
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@ -232,7 +234,60 @@ print(soc_eauto)
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pprint(o)
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pprint(eauto.get_stuendlicher_soc())
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visualisiere_ergebnisse(gesamtlast,leistung_haushalt,leistung_wp, pv_forecast, specific_date_prices, o,soc_eauto,best_solution[0::2],best_solution[1::2] )
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# # Werkzeug-Setup
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# creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
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# creator.create("Individual", list, fitness=creator.FitnessMin)
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# toolbox = base.Toolbox()
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# toolbox.register("attr_bool", random.randint, 0, 1)
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# toolbox.register("attr_float", random.uniform, 0.0, 1.0)
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# toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_bool,toolbox.attr_float), n=prediction_hours)
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# start_individual = toolbox.individual()
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# start_individual[:] = start_solution
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# toolbox.register("population", tools.initRepeat, list, toolbox.individual)
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# toolbox.register("evaluate", evaluate)
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# toolbox.register("mate", tools.cxTwoPoint)
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# toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
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# toolbox.register("select", tools.selTournament, tournsize=3)
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# # Genetischer Algorithmus
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# def optimize():
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# population = toolbox.population(n=1000)
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# population[0] = start_individual
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# hof = tools.HallOfFame(1)
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# stats = tools.Statistics(lambda ind: ind.fitness.values)
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# stats.register("avg", np.mean)
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# stats.register("min", np.min)
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# stats.register("max", np.max)
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# algorithms.eaMuPlusLambda(population, toolbox, 100, 200, cxpb=0.5, mutpb=0.2, ngen=1000, stats=stats, halloffame=hof, verbose=True)
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# #algorithms.eaSimple(population, toolbox, cxpb=0.2, mutpb=0.2, ngen=1000, stats=stats, halloffame=hof, verbose=True)
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# return hof[0]
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# best_solution = optimize()
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# print("Beste Lösung:", best_solution)
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# #ems.set_akku_discharge_hours(best_solution)
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# o,eauto = evaluate_inner(best_solution)
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# soc_eauto = eauto.get_stuendlicher_soc()
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# print(soc_eauto)
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# pprint(o)
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# pprint(eauto.get_stuendlicher_soc())
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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)
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# for data in forecast.get_forecast_data():
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