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Anfangs und Endzustand jeder Stunden berücksichtigt.

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Bla Bla
2024-08-31 10:40:07 +02:00
parent 65ecf4cf3c
commit c03d5f386a
4 changed files with 56 additions and 265 deletions
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12
modules/class_optimize.py
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@@ -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":[]}