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48 Stunden Predcition & Optimierung

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Ein paar Zeitfunktionen korrigiert (24h / 48h)
Strompreis Cache stündlich leeren
Strompreis bei nur 24h Daten, wird verdoppelt (Prognose fehlt noch)
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Bla Bla
2024-05-08 09:58:41 +02:00
parent 817e5e3cc9
commit 95b0ec5664
6 changed files with 79 additions and 46 deletions
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31
modules/class_optimize.py
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@@ -70,7 +70,7 @@ class optimization_problem:
self.toolbox.register("individual", create_individual)#tools.initCycle, creator.Individual, (self.toolbox.attr_bool,self.toolbox.attr_bool), n=self.prediction_hours+1)
self.toolbox.register("population", tools.initRepeat, list, self.toolbox.individual)
self.toolbox.register("mate", tools.cxTwoPoint)
self.toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
self.toolbox.register("mutate", tools.mutFlipBit, indpb=0.1)
self.toolbox.register("select", tools.selTournament, tournsize=3)
def evaluate_inner(self,individual, ems,start_hour):
@@ -131,7 +131,7 @@ class optimization_problem:
# Genetischer Algorithmus
def optimize(self,start_solution=None):
population = self.toolbox.population(n=1000)
population = self.toolbox.population(n=400)
hof = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
@@ -145,7 +145,7 @@ class optimization_problem:
population.insert(0, creator.Individual(start_solution))
#algorithms.eaMuPlusLambda(population, self.toolbox, 100, 200, cxpb=0.2, mutpb=0.2, ngen=500, stats=stats, halloffame=hof, verbose=True)
algorithms.eaSimple(population, self.toolbox, cxpb=0.2, mutpb=0.2, ngen=200, stats=stats, halloffame=hof, verbose=True)
algorithms.eaSimple(population, self.toolbox, cxpb=0.1, mutpb=0.1, ngen=400, stats=stats, halloffame=hof, verbose=True)
member = {"bilanz":[],"verluste":[],"nebenbedingung":[]}
for ind in population:
@@ -159,14 +159,19 @@ class optimization_problem:
return hof[0], member
def optimierung_ems(self,parameter=None, start_hour=None,worst_case=False):
def optimierung_ems(self,parameter=None, start_hour=None,worst_case=False, startdate=None):
############
# Parameter
############
date = (datetime.now().date() + timedelta(hours = self.prediction_hours)).strftime("%Y-%m-%d")
date_now = datetime.now().strftime("%Y-%m-%d")
if startdate == None:
date = (datetime.now().date() + timedelta(hours = self.prediction_hours)).strftime("%Y-%m-%d")
date_now = datetime.now().strftime("%Y-%m-%d")
else:
date = (startdate + timedelta(hours = self.prediction_hours)).strftime("%Y-%m-%d")
date_now = startdate.strftime("%Y-%m-%d")
#print("Start_date:",date_now)
akku_size = parameter['pv_akku_cap'] # Wh
year_energy = parameter['year_energy'] #2000*1000 #Wh
@@ -187,7 +192,7 @@ class optimization_problem:
eauto.set_charge_per_hour(laden_moeglich)
min_soc_eauto = parameter['eauto_min_soc']
start_params = parameter['start_solution']
gesamtlast = Gesamtlast()
gesamtlast = Gesamtlast(prediction_hours=self.prediction_hours)
###############
# spuelmaschine
@@ -205,7 +210,12 @@ class optimization_problem:
###############
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()
leistung_haushalt = lf.get_stats_for_date_range(date_now,date)[0] # Nur Erwartungswert!
gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
###############
@@ -229,8 +239,11 @@ class optimization_problem:
###############
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+"")
price_forecast = HourlyElectricityPriceForecast(source="https://api.akkudoktor.net/prices?start="+date_now+"&end="+date+"", prediction_hours=self.prediction_hours)
specific_date_prices = price_forecast.get_price_for_daterange(date_now,date)
#print(price_forecast)
print(specific_date_prices)
#print("https://api.akkudoktor.net/prices?start="+date_now+"&end="+date)
###############
# WP