mirror of
https://github.com/Akkudoktor-EOS/EOS.git
synced 2025-06-28 00:46:53 +00:00
Anfangs und Endzustand jeder Stunden berücksichtigt.
This commit is contained in:
Bla Bla
parent
cfc2a4d0b1
commit
86a31df479
@ -1,5 +1,17 @@
|
||||
from datetime import datetime
|
||||
from pprint import pprint
|
||||
import numpy as np
|
||||
|
||||
def replace_nan_with_none(data):
|
||||
if isinstance(data, dict):
|
||||
return {key: replace_nan_with_none(value) for key, value in data.items()}
|
||||
elif isinstance(data, list):
|
||||
return [replace_nan_with_none(element) for element in data]
|
||||
elif isinstance(data, float) and np.isnan(data):
|
||||
return None
|
||||
else:
|
||||
return data
|
||||
|
||||
|
||||
|
||||
class EnergieManagementSystem:
|
||||
@ -58,10 +70,29 @@ class EnergieManagementSystem:
|
||||
|
||||
ende = min( len(lastkurve_wh),len(self.pv_prognose_wh), len(self.strompreis_euro_pro_wh))
|
||||
|
||||
# Endzustände auf NaN setzen, damit diese übersprungen werden für die Stunde
|
||||
last_wh_pro_stunde.append(np.nan)
|
||||
netzeinspeisung_wh_pro_stunde.append(np.nan)
|
||||
netzbezug_wh_pro_stunde.append(np.nan)
|
||||
kosten_euro_pro_stunde.append(np.nan)
|
||||
akku_soc_pro_stunde.append(self.akku.ladezustand_in_prozent())
|
||||
einnahmen_euro_pro_stunde.append(np.nan)
|
||||
eauto_soc_pro_stunde.append(self.eauto.ladezustand_in_prozent())
|
||||
verluste_wh_pro_stunde.append(np.nan)
|
||||
haushaltsgeraet_wh_pro_stunde.append(np.nan)
|
||||
|
||||
# Berechnet das Ende basierend auf der Länge der Lastkurve
|
||||
for stunde in range(start_stunde, ende):
|
||||
for stunde in range(start_stunde+1, ende):
|
||||
|
||||
# Zustand zu Beginn der Stunde (Anfangszustand)
|
||||
akku_soc_start = self.akku.ladezustand_in_prozent() # Anfangszustand Akku-SoC
|
||||
if self.eauto:
|
||||
eauto_soc_start = self.eauto.ladezustand_in_prozent() # Anfangszustand E-Auto-SoC
|
||||
|
||||
|
||||
|
||||
# Anpassung, um sicherzustellen, dass Indizes korrekt sind
|
||||
verbrauch = lastkurve_wh[stunde]
|
||||
verbrauch = lastkurve_wh[stunde] # Verbrauch für die Stunde
|
||||
if self.haushaltsgeraet != None:
|
||||
verbrauch = verbrauch + self.haushaltsgeraet.get_last_fuer_stunde(stunde)
|
||||
haushaltsgeraet_wh_pro_stunde.append(self.haushaltsgeraet.get_last_fuer_stunde(stunde))
|
||||
@ -69,8 +100,8 @@ class EnergieManagementSystem:
|
||||
haushaltsgeraet_wh_pro_stunde.append(0)
|
||||
erzeugung = self.pv_prognose_wh[stunde]
|
||||
strompreis = self.strompreis_euro_pro_wh[stunde] if stunde < len(self.strompreis_euro_pro_wh) else self.strompreis_euro_pro_wh[-1]
|
||||
verluste_wh_pro_stunde.append(0.0)
|
||||
|
||||
verluste_wh_pro_stunde.append(0.0)
|
||||
|
||||
# Logik für die E-Auto-Ladung bzw. Entladung
|
||||
if self.eauto: # Falls ein E-Auto vorhanden ist
|
||||
@ -108,7 +139,7 @@ class EnergieManagementSystem:
|
||||
einnahmen_euro_pro_stunde.append(stündliche_einnahmen_euro)
|
||||
|
||||
|
||||
gesamtkosten_euro = sum(kosten_euro_pro_stunde) - sum(einnahmen_euro_pro_stunde)
|
||||
gesamtkosten_euro = np.nansum(kosten_euro_pro_stunde) - np.nansum(einnahmen_euro_pro_stunde)
|
||||
expected_length = ende - start_stunde
|
||||
array_names = ['Eigenverbrauch_Wh_pro_Stunde', 'Netzeinspeisung_Wh_pro_Stunde', 'Netzbezug_Wh_pro_Stunde', 'Kosten_Euro_pro_Stunde', 'akku_soc_pro_stunde', 'Einnahmen_Euro_pro_Stunde','E-Auto_SoC_pro_Stunde', "Verluste_Pro_Stunde"]
|
||||
all_arrays = [last_wh_pro_stunde, netzeinspeisung_wh_pro_stunde, netzbezug_wh_pro_stunde, kosten_euro_pro_stunde, akku_soc_pro_stunde, einnahmen_euro_pro_stunde,eauto_soc_pro_stunde,verluste_wh_pro_stunde]
|
||||
@ -128,11 +159,13 @@ class EnergieManagementSystem:
|
||||
'Einnahmen_Euro_pro_Stunde': einnahmen_euro_pro_stunde,
|
||||
'Gesamtbilanz_Euro': gesamtkosten_euro,
|
||||
'E-Auto_SoC_pro_Stunde':eauto_soc_pro_stunde,
|
||||
'Gesamteinnahmen_Euro': sum(einnahmen_euro_pro_stunde),
|
||||
'Gesamtkosten_Euro': sum(kosten_euro_pro_stunde),
|
||||
'Gesamteinnahmen_Euro': np.nansum(einnahmen_euro_pro_stunde),
|
||||
'Gesamtkosten_Euro': np.nansum(kosten_euro_pro_stunde),
|
||||
"Verluste_Pro_Stunde":verluste_wh_pro_stunde,
|
||||
"Gesamt_Verluste":sum(verluste_wh_pro_stunde),
|
||||
"Gesamt_Verluste":np.nansum(verluste_wh_pro_stunde),
|
||||
"Haushaltsgeraet_wh_pro_stunde":haushaltsgeraet_wh_pro_stunde
|
||||
}
|
||||
|
||||
out = replace_nan_with_none(out)
|
||||
|
||||
return out
|
||||
|
||||
@ -154,8 +154,16 @@ class optimization_problem:
|
||||
final_soc = ems.eauto.ladezustand_in_prozent() # Nimmt den SoC am Ende des Optimierungszeitraums
|
||||
|
||||
|
||||
eauto_roi = max(0,(parameter['eauto_min_soc']-ems.eauto.ladezustand_in_prozent()) )
|
||||
|
||||
|
||||
if (parameter['eauto_min_soc']-ems.eauto.ladezustand_in_prozent()) <= 0.0:
|
||||
#print (parameter['eauto_min_soc']," " ,ems.eauto.ladezustand_in_prozent()," ",(parameter['eauto_min_soc']-ems.eauto.ladezustand_in_prozent()))
|
||||
for i in range(0, self.prediction_hours):
|
||||
if eautocharge_hours_float[i] != 0.0: # Wenn die letzten x Stunden von einem festen Wert abweichen
|
||||
gesamtbilanz += self.strafe # Bestrafe den Optimierer
|
||||
|
||||
|
||||
eauto_roi = (parameter['eauto_min_soc']-ems.eauto.ladezustand_in_prozent())
|
||||
individual.extra_data = (o["Gesamtbilanz_Euro"],o["Gesamt_Verluste"], eauto_roi )
|
||||
|
||||
|
||||
@ -191,7 +199,7 @@ class optimization_problem:
|
||||
if start_solution is not None and start_solution != -1:
|
||||
population.insert(0, creator.Individual(start_solution))
|
||||
|
||||
algorithms.eaMuPlusLambda(population, self.toolbox, 100, 200, cxpb=0.2, mutpb=0.2, ngen=1000, stats=stats, halloffame=hof, verbose=True)
|
||||
algorithms.eaMuPlusLambda(population, self.toolbox, mu=200, lambda_=300, cxpb=0.3, mutpb=0.4, ngen=500, stats=stats, halloffame=hof, verbose=True)
|
||||
#algorithms.eaSimple(population, self.toolbox, cxpb=0.2, mutpb=0.2, ngen=1000, stats=stats, halloffame=hof, verbose=True)
|
||||
|
||||
member = {"bilanz":[],"verluste":[],"nebenbedingung":[]}
|
||||
|
||||
@ -113,7 +113,8 @@ def visualisiere_ergebnisse(gesamtlast, pv_forecast, strompreise, ergebnisse, d
|
||||
plt.subplot(3, 2, 2)
|
||||
plt.plot(stunden, ergebnisse['akku_soc_pro_stunde'], label='PV Akku (%)', marker='x')
|
||||
plt.plot(stunden, ergebnisse['E-Auto_SoC_pro_Stunde'], label='E-Auto Akku (%)', marker='x')
|
||||
plt.legend(loc='upper left')
|
||||
plt.legend(loc='upper left', bbox_to_anchor=(1, 1)) # Legende außerhalb des Plots platzieren
|
||||
plt.grid(True, which='both', axis='x') # Grid für jede Stunde
|
||||
|
||||
ax1 = plt.subplot(3, 2, 3)
|
||||
for hour, value in enumerate(discharge_hours):
|
||||
|
||||
259
test.py
259
test.py
@ -23,7 +23,7 @@ import os
|
||||
|
||||
|
||||
|
||||
start_hour = 11
|
||||
start_hour = 13
|
||||
|
||||
pv_forecast= [
|
||||
0,
|
||||
@ -325,262 +325,11 @@ start_solution= [
|
||||
1,
|
||||
1
|
||||
]
|
||||
parameter= {"preis_euro_pro_wh_akku": 30e-05,'pv_soc': 60.4052, 'pv_akku_cap': 30000, 'year_energy': 4100000, 'einspeiseverguetung_euro_pro_wh': 7e-05, 'max_heizleistung': 1000,"gesamtlast":gesamtlast, 'pv_forecast': pv_forecast, "temperature_forecast":temperature_forecast, "strompreis_euro_pro_wh":strompreis_euro_pro_wh, 'eauto_min_soc': 80, 'eauto_cap': 60000, 'eauto_charge_efficiency': 0.95, 'eauto_charge_power': 7590, 'eauto_soc': 53, 'pvpowernow': 211.137503624, 'start_solution': start_solution, 'haushaltsgeraet_wh': 937, 'haushaltsgeraet_dauer': 0}
|
||||
parameter= {"preis_euro_pro_wh_akku": 30e-05,'pv_soc': 95.00, 'pv_akku_cap': 30000, 'year_energy': 4100000, 'einspeiseverguetung_euro_pro_wh': 7e-05, 'max_heizleistung': 1000,"gesamtlast":gesamtlast, 'pv_forecast': pv_forecast, "temperature_forecast":temperature_forecast, "strompreis_euro_pro_wh":strompreis_euro_pro_wh, 'eauto_min_soc': 0, 'eauto_cap': 60000, 'eauto_charge_efficiency': 0.95, 'eauto_charge_power': 7590, 'eauto_soc': 53, 'pvpowernow': 211.137503624, 'start_solution': start_solution, 'haushaltsgeraet_wh': 937, 'haushaltsgeraet_dauer': 0}
|
||||
|
||||
|
||||
|
||||
opt_class = optimization_problem(prediction_hours=48, strafe=10,optimization_hours=24)
|
||||
ergebnis = opt_class.optimierung_ems(parameter=parameter, start_hour=start_hour)
|
||||
|
||||
|
||||
# #Gesamtlast
|
||||
# #############
|
||||
# gesamtlast = Gesamtlast()
|
||||
|
||||
|
||||
# # 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,...].flatten()
|
||||
# # print(date_now," ",date)
|
||||
# # print(leistung_haushalt.shape)
|
||||
# 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=52.52&lon=13.405&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)
|
||||
|
||||
|
||||
|
||||
# # Strompreise
|
||||
# ###############
|
||||
# filepath = os.path.join (r'test_data', r'strompreise_akkudokAPI.json') # Pfad zur JSON-Datei anpassen
|
||||
# #price_forecast = HourlyElectricityPriceForecast(source=filepath)
|
||||
# price_forecast = HourlyElectricityPriceForecast(source="https://api.akkudoktor.net/prices?start="+date_now+"&end="+date+"")
|
||||
# specific_date_prices = price_forecast.get_price_for_daterange(date_now,date)
|
||||
# # print("13:",specific_date_prices[13])
|
||||
# # print("14:",specific_date_prices[14])
|
||||
# # print("15:",specific_date_prices[15])
|
||||
# # sys.exit()
|
||||
# # WP
|
||||
# ##############
|
||||
# leistung_wp = wp.simulate_24h(temperature_forecast)
|
||||
# gesamtlast.hinzufuegen("Heatpump", leistung_wp)
|
||||
|
||||
|
||||
# # EAuto
|
||||
# ######################
|
||||
# # leistung_eauto = eauto.get_stuendliche_last()
|
||||
# # soc_eauto = eauto.get_stuendlicher_soc()
|
||||
# # gesamtlast.hinzufuegen("eauto", leistung_eauto)
|
||||
|
||||
# # print(gesamtlast.gesamtlast_berechnen())
|
||||
|
||||
# # EMS / Stromzähler Bilanz
|
||||
# #akku=None, pv_prognose_wh=None, strompreis_cent_pro_wh=None, einspeiseverguetung_cent_pro_wh=None, eauto=None, gesamtlast=None
|
||||
|
||||
# ems = EnergieManagementSystem(akku=akku, gesamtlast = gesamtlast, pv_prognose_wh=pv_forecast, strompreis_cent_pro_wh=specific_date_prices, einspeiseverguetung_cent_pro_wh=einspeiseverguetung_cent_pro_wh, eauto=eauto)
|
||||
|
||||
|
||||
# o = ems.simuliere(start_hour)#ems.simuliere_ab_jetzt()
|
||||
# #pprint(o)
|
||||
# #pprint(o["Gesamtbilanz_Euro"])
|
||||
|
||||
# #visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,discharge_array,laden_moeglich, temperature_forecast, start_hour, prediction_hours)
|
||||
|
||||
|
||||
|
||||
# # Optimierung
|
||||
|
||||
# def evaluate_inner(individual):
|
||||
# #print(individual)
|
||||
# discharge_hours_bin = individual[0::2]
|
||||
# eautocharge_hours_float = individual[1::2]
|
||||
|
||||
# #print(discharge_hours_bin)
|
||||
# #print(len(eautocharge_hours_float))
|
||||
# ems.reset()
|
||||
# #eauto.reset()
|
||||
# ems.set_akku_discharge_hours(discharge_hours_bin)
|
||||
# ems.set_eauto_charge_hours(eautocharge_hours_float)
|
||||
|
||||
# #eauto.set_laden_moeglich(eautocharge_hours_float)
|
||||
# #eauto.berechne_ladevorgang()
|
||||
# #leistung_eauto = eauto.get_stuendliche_last()
|
||||
# #gesamtlast.hinzufuegen("eauto", leistung_eauto)
|
||||
|
||||
# #ems.set_gesamtlast(gesamtlast.gesamtlast_berechnen())
|
||||
|
||||
# o = ems.simuliere(start_hour)
|
||||
# return o, eauto
|
||||
|
||||
# # Fitness-Funktion (muss Ihre EnergieManagementSystem-Logik integrieren)
|
||||
# def evaluate(individual):
|
||||
# o,eauto = evaluate_inner(individual)
|
||||
# gesamtbilanz = o["Gesamtbilanz_Euro"]
|
||||
|
||||
# # Überprüfung, ob der Mindest-SoC erreicht wird
|
||||
# final_soc = eauto.ladezustand_in_prozent() # Nimmt den SoC am Ende des Optimierungszeitraums
|
||||
# strafe = 0.0
|
||||
# #if final_soc < min_soc_eauto:
|
||||
# # Fügt eine Strafe hinzu, wenn der Mindest-SoC nicht erreicht wird
|
||||
# strafe = max(0,(min_soc_eauto - final_soc) * hohe_strafe ) # `hohe_strafe` ist ein vorher festgelegter Strafwert
|
||||
# gesamtbilanz += strafe
|
||||
# gesamtbilanz += o["Gesamt_Verluste"]/1000.0
|
||||
# return (gesamtbilanz,)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# # 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_bool", random.randint, 0, 1)
|
||||
# toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_bool,toolbox.attr_bool), n=prediction_hours)
|
||||
|
||||
|
||||
|
||||
# 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=500)
|
||||
# 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, 50, 100, cxpb=0.5, mutpb=0.5, ngen=500, 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]
|
||||
|
||||
|
||||
# start_solution = optimize()
|
||||
|
||||
|
||||
# print("Start Lösung:", start_solution)
|
||||
|
||||
|
||||
|
||||
|
||||
# # # 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()
|
||||
# best_solution = start_solution
|
||||
# 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)
|
||||
# visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,best_solution[0::2],best_solution[1::2] , temperature_forecast, start_hour, prediction_hours)
|
||||
|
||||
|
||||
# # for data in forecast.get_forecast_data():
|
||||
# # print(data.get_date_time(), data.get_dc_power(), data.get_ac_power(), data.get_windspeed_10m(), data.get_temperature())for data in forecast.get_forecast_data():
|
||||
|
||||
|
||||
|
||||
|
||||
# # app = Flask(__name__)
|
||||
|
||||
|
||||
|
||||
# # @app.route('/getdata', methods=['GET'])
|
||||
# # def get_data():
|
||||
# # # Hole das Datum aus den Query-Parametern
|
||||
# # date_str = request.args.get('date')
|
||||
# # year_energy = request.args.get('year_energy')
|
||||
|
||||
# # try:
|
||||
# # # Konvertiere das Datum in ein datetime-Objekt
|
||||
# # date_obj = datetime.strptime(date_str, '%Y-%m-%d')
|
||||
# # filepath = r'.\load_profiles.npz' # Pfad zur JSON-Datei anpassen
|
||||
# # lf = cl.LoadForecast(filepath=filepath, year_energy=float(year_energy))
|
||||
# # specific_date_prices = lf.get_daily_stats('2024-02-16')
|
||||
|
||||
|
||||
# # # Berechne den Tag des Jahres
|
||||
# # #day_of_year = date_obj.timetuple().tm_yday
|
||||
|
||||
# # # Konvertiere den Tag des Jahres in einen String, falls die Schlüssel als Strings gespeichert sind
|
||||
# # #day_key = int(day_of_year)
|
||||
# # #print(day_key)
|
||||
# # # Überprüfe, ob der Tag im Jahr in den Daten vorhanden ist
|
||||
# # array_list = lf.get_daily_stats(date_str)
|
||||
# # pprint(array_list)
|
||||
# # pprint(array_list.shape)
|
||||
# # if array_list.shape == (2,24):
|
||||
# # #if day_key < len(load_profiles_exp):
|
||||
# # # Konvertiere das Array in eine Liste für die JSON-Antwort
|
||||
# # #((load_profiles_exp_l[day_key]).tolist(),(load_profiles_std_l)[day_key].tolist())
|
||||
|
||||
# # return jsonify({date_str: array_list.tolist()})
|
||||
# # else:
|
||||
# # return jsonify({"error": "Datum nicht gefunden"}), 404
|
||||
# # except ValueError:
|
||||
# # # Wenn das Datum nicht im richtigen Format ist oder ungültig ist
|
||||
# # return jsonify({"error": "Ungültiges Datum"}), 400
|
||||
|
||||
# # if __name__ == '__main__':
|
||||
# # app.run(debug=True)
|
||||
|
||||
print(ergebnis)
|
||||
print(jsonify(ergebnis))
|
||||
Loading…
x
Reference in New Issue
Block a user