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https://github.com/Akkudoktor-EOS/EOS.git
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Inverter Klasse hinzugefügt
Kleinere Bugs bei max_WR Leistung behoben
This commit is contained in:
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446
test.py
446
test.py
@@ -1,14 +1,16 @@
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from flask import Flask, jsonify, request
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import numpy as np
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from datetime import datetime
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from modules.class_load import *
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from modules.class_ems import *
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from modules.class_pv_forecast import *
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from modules.class_akku import *
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from modules.class_strompreis import *
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from modules.class_heatpump import *
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from modules.class_load_container import *
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from modules.class_eauto import *
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from modules.class_optimize import *
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# from modules.class_load import *
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# from modules.class_ems import *
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# from modules.class_pv_forecast import *
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# from modules.class_akku import *
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# from modules.class_strompreis import *
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# from modules.class_heatpump import *
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# from modules.class_load_container import *
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# from modules.class_eauto import *
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from modules.class_optimize import *
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from pprint import pprint
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import matplotlib.pyplot as plt
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@@ -19,193 +21,165 @@ import random
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import os
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start_hour = 11
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prediction_hours = 24
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date = (datetime.now().date() + timedelta(hours = prediction_hours)).strftime("%Y-%m-%d")
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date_now = datetime.now().strftime("%Y-%m-%d")
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start_hour = 8
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# prediction_hours = 24
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# date = (datetime.now().date() + timedelta(hours = prediction_hours)).strftime("%Y-%m-%d")
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# date_now = datetime.now().strftime("%Y-%m-%d")
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akku_size = 30000 # Wh
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year_energy = 2000*1000 #Wh
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einspeiseverguetung_cent_pro_wh = np.full(prediction_hours, 7/(1000.0*100.0)) # € / Wh
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# akku_size = 30000 # Wh
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# year_energy = 2000*1000 #Wh
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# einspeiseverguetung_cent_pro_wh = np.full(prediction_hours, 7/(1000.0*100.0)) # € / Wh
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max_heizleistung = 1000 # 5 kW Heizleistung
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wp = Waermepumpe(max_heizleistung,prediction_hours)
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# max_heizleistung = 1000 # 5 kW Heizleistung
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# wp = Waermepumpe(max_heizleistung,prediction_hours)
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akku = PVAkku(akku_size,prediction_hours)
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discharge_array = np.full(prediction_hours,1) #np.array([1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0]) #
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# akku = PVAkku(akku_size,prediction_hours)
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# discharge_array = np.full(prediction_hours,1) #np.array([1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0]) #
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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 = PVAkku(kapazitaet_wh=60000, hours=prediction_hours, lade_effizienz=0.95, entlade_effizienz=1.0, max_ladeleistung_w=10000 ,start_soc_prozent=10)
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eauto.set_charge_per_hour(laden_moeglich)
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min_soc_eauto = 80
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hohe_strafe = 10.0
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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 = PVAkku(kapazitaet_wh=60000, hours=prediction_hours, lade_effizienz=0.95, entlade_effizienz=1.0, max_ladeleistung_w=10000 ,start_soc_prozent=10)
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# eauto.set_charge_per_hour(laden_moeglich)
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# min_soc_eauto = 80
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# hohe_strafe = 10.0
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#[1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]
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individual = [1, 1, # 0
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0, 1, # 1
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0, 0, # 2
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0, 1, # 3
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0, 0, # 4
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1, 0, # 5
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0, 1, # 6
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0, 0, # 7
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0, 0, # 8
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1, 0,
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0, 0,
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1, 0,
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0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]
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parameter= {'pv_soc': 92.4052, 'pv_akku_cap': 30000, 'year_energy': 4100000, 'einspeiseverguetung_euro_pro_wh': 7e-05, 'max_heizleistung': 1000, 'pv_forecast_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', 'eauto_min_soc': 100, 'eauto_cap': 60000, 'eauto_charge_efficiency': 0.95, 'eauto_charge_power': 5500, 'eauto_soc': 77, 'pvpowernow': 211.137503624, 'start_solution': individual, 'haushaltsgeraet_wh': 937, 'haushaltsgeraet_dauer': 0}
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#Gesamtlast
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#############
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gesamtlast = Gesamtlast()
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opt_class = optimization_problem(prediction_hours=24, strafe=10)
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ergebnis = opt_class.optimierung_ems(parameter=parameter, start_hour=start_hour)
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# Load Forecast
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###############
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lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
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#leistung_haushalt = lf.get_daily_stats(date)[0,...] # Datum anpassen
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leistung_haushalt = lf.get_stats_for_date_range(date_now,date)[0,...].flatten()
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# print(date_now," ",date)
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# print(leistung_haushalt.shape)
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gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
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# #Gesamtlast
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# #############
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# gesamtlast = Gesamtlast()
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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=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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# # Load Forecast
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# ###############
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# lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
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# #leistung_haushalt = lf.get_daily_stats(date)[0,...] # Datum anpassen
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# leistung_haushalt = lf.get_stats_for_date_range(date_now,date)[0,...].flatten()
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# # print(date_now," ",date)
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# # print(leistung_haushalt.shape)
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# 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=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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# Strompreise
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###############
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filepath = os.path.join (r'test_data', r'strompreise_akkudokAPI.json') # Pfad zur JSON-Datei anpassen
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#price_forecast = HourlyElectricityPriceForecast(source=filepath)
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price_forecast = HourlyElectricityPriceForecast(source="https://api.akkudoktor.net/prices?start="+date_now+"&end="+date+"")
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specific_date_prices = price_forecast.get_price_for_daterange(date_now,date)
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# print("13:",specific_date_prices[13])
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# print("14:",specific_date_prices[14])
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# print("15:",specific_date_prices[15])
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# sys.exit()
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# WP
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##############
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leistung_wp = wp.simulate_24h(temperature_forecast)
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gesamtlast.hinzufuegen("Heatpump", leistung_wp)
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# # Strompreise
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# ###############
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# filepath = os.path.join (r'test_data', r'strompreise_akkudokAPI.json') # Pfad zur JSON-Datei anpassen
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# #price_forecast = HourlyElectricityPriceForecast(source=filepath)
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# price_forecast = HourlyElectricityPriceForecast(source="https://api.akkudoktor.net/prices?start="+date_now+"&end="+date+"")
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# specific_date_prices = price_forecast.get_price_for_daterange(date_now,date)
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# # print("13:",specific_date_prices[13])
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# # print("14:",specific_date_prices[14])
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# # print("15:",specific_date_prices[15])
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# # sys.exit()
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# # WP
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# ##############
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# leistung_wp = wp.simulate_24h(temperature_forecast)
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# gesamtlast.hinzufuegen("Heatpump", leistung_wp)
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# EAuto
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######################
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# leistung_eauto = eauto.get_stuendliche_last()
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# soc_eauto = eauto.get_stuendlicher_soc()
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# gesamtlast.hinzufuegen("eauto", leistung_eauto)
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# # EAuto
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# ######################
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# # leistung_eauto = eauto.get_stuendliche_last()
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# # soc_eauto = eauto.get_stuendlicher_soc()
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# # gesamtlast.hinzufuegen("eauto", leistung_eauto)
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# print(gesamtlast.gesamtlast_berechnen())
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# # print(gesamtlast.gesamtlast_berechnen())
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# EMS / Stromzähler Bilanz
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#akku=None, pv_prognose_wh=None, strompreis_cent_pro_wh=None, einspeiseverguetung_cent_pro_wh=None, eauto=None, gesamtlast=None
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# # EMS / Stromzähler Bilanz
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# #akku=None, pv_prognose_wh=None, strompreis_cent_pro_wh=None, einspeiseverguetung_cent_pro_wh=None, eauto=None, gesamtlast=None
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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)
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# 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)
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o = ems.simuliere(start_hour)#ems.simuliere_ab_jetzt()
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#pprint(o)
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#pprint(o["Gesamtbilanz_Euro"])
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# o = ems.simuliere(start_hour)#ems.simuliere_ab_jetzt()
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# #pprint(o)
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# #pprint(o["Gesamtbilanz_Euro"])
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#visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,discharge_array,laden_moeglich, temperature_forecast, start_hour, prediction_hours)
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# #visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,discharge_array,laden_moeglich, temperature_forecast, start_hour, prediction_hours)
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# Optimierung
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# # Optimierung
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def evaluate_inner(individual):
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#print(individual)
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discharge_hours_bin = individual[0::2]
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eautocharge_hours_float = individual[1::2]
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# def evaluate_inner(individual):
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# #print(individual)
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# discharge_hours_bin = individual[0::2]
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# eautocharge_hours_float = individual[1::2]
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#print(discharge_hours_bin)
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#print(len(eautocharge_hours_float))
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ems.reset()
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#eauto.reset()
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ems.set_akku_discharge_hours(discharge_hours_bin)
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ems.set_eauto_charge_hours(eautocharge_hours_float)
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# #print(discharge_hours_bin)
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# #print(len(eautocharge_hours_float))
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# ems.reset()
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# #eauto.reset()
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# ems.set_akku_discharge_hours(discharge_hours_bin)
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# ems.set_eauto_charge_hours(eautocharge_hours_float)
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#eauto.set_laden_moeglich(eautocharge_hours_float)
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#eauto.berechne_ladevorgang()
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#leistung_eauto = eauto.get_stuendliche_last()
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#gesamtlast.hinzufuegen("eauto", leistung_eauto)
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# #eauto.set_laden_moeglich(eautocharge_hours_float)
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# #eauto.berechne_ladevorgang()
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# #leistung_eauto = eauto.get_stuendliche_last()
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# #gesamtlast.hinzufuegen("eauto", leistung_eauto)
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#ems.set_gesamtlast(gesamtlast.gesamtlast_berechnen())
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# #ems.set_gesamtlast(gesamtlast.gesamtlast_berechnen())
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o = ems.simuliere(start_hour)
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return o, eauto
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# o = ems.simuliere(start_hour)
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# return o, eauto
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# Fitness-Funktion (muss Ihre EnergieManagementSystem-Logik integrieren)
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def evaluate(individual):
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o,eauto = evaluate_inner(individual)
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gesamtbilanz = o["Gesamtbilanz_Euro"]
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# # Fitness-Funktion (muss Ihre EnergieManagementSystem-Logik integrieren)
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# def evaluate(individual):
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# o,eauto = evaluate_inner(individual)
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# gesamtbilanz = o["Gesamtbilanz_Euro"]
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# Überprüfung, ob der Mindest-SoC erreicht wird
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final_soc = eauto.ladezustand_in_prozent() # Nimmt den SoC am Ende des Optimierungszeitraums
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strafe = 0.0
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#if final_soc < min_soc_eauto:
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# Fügt eine Strafe hinzu, wenn der Mindest-SoC nicht erreicht wird
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strafe = max(0,(min_soc_eauto - final_soc) * hohe_strafe ) # `hohe_strafe` ist ein vorher festgelegter Strafwert
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gesamtbilanz += strafe
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gesamtbilanz += o["Gesamt_Verluste"]/1000.0
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return (gesamtbilanz,)
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# # Überprüfung, ob der Mindest-SoC erreicht wird
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# final_soc = eauto.ladezustand_in_prozent() # Nimmt den SoC am Ende des Optimierungszeitraums
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# strafe = 0.0
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# #if final_soc < min_soc_eauto:
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# # Fügt eine Strafe hinzu, wenn der Mindest-SoC nicht erreicht wird
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# strafe = max(0,(min_soc_eauto - final_soc) * hohe_strafe ) # `hohe_strafe` ist ein vorher festgelegter Strafwert
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# gesamtbilanz += strafe
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# gesamtbilanz += o["Gesamt_Verluste"]/1000.0
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# return (gesamtbilanz,)
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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_bool", random.randint, 0, 1)
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toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_bool,toolbox.attr_bool), n=prediction_hours)
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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=500)
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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, 50, 100, cxpb=0.5, mutpb=0.5, ngen=500, 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)
|
||||
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)
|
||||
# toolbox.register("attr_bool", random.randint, 0, 1)
|
||||
# toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_bool,toolbox.attr_bool), n=prediction_hours)
|
||||
|
||||
|
||||
# start_individual = toolbox.individual()
|
||||
# start_individual[:] = start_solution
|
||||
|
||||
# toolbox.register("population", tools.initRepeat, list, toolbox.individual)
|
||||
|
||||
@@ -216,8 +190,7 @@ print("Start Lösung:", start_solution)
|
||||
|
||||
# # Genetischer Algorithmus
|
||||
# def optimize():
|
||||
# population = toolbox.population(n=1000)
|
||||
# population[0] = start_individual
|
||||
# population = toolbox.population(n=500)
|
||||
# hof = tools.HallOfFame(1)
|
||||
|
||||
# stats = tools.Statistics(lambda ind: ind.fitness.values)
|
||||
@@ -225,73 +198,120 @@ print("Start Lösung:", start_solution)
|
||||
# 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.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]
|
||||
|
||||
# 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')
|
||||
|
||||
# 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
|
||||
# # # 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())
|
||||
# # # 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
|
||||
# # 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)
|
||||
# # if __name__ == '__main__':
|
||||
# # app.run(debug=True)
|
||||
|
||||
|
||||
Reference in New Issue
Block a user