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PV Forecast mit URL möglich + persistenter Cache eingebaut
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@ -45,7 +45,7 @@ class EnergieManagementSystem:
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def reset(self):
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self.akku.reset()
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def simuliere(self):
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def simuliere(self, start_stunde):
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eigenverbrauch_wh_pro_stunde = []
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netzeinspeisung_wh_pro_stunde = []
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netzbezug_wh_pro_stunde = []
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@ -53,10 +53,18 @@ class EnergieManagementSystem:
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einnahmen_euro_pro_stunde = []
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akku_soc_pro_stunde = []
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for stunde in range(len(self.lastkurve_wh)):
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ende = len(self.lastkurve_wh) # Berechnet das Ende basierend auf der Länge der Lastkurve
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for stunde in range(start_stunde, ende):
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# Anpassung, um sicherzustellen, dass Indizes korrekt sind
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verbrauch = self.lastkurve_wh[stunde]
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erzeugung = self.pv_prognose_wh[stunde]
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strompreis = self.strompreis_cent_pro_wh[stunde]
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strompreis = self.strompreis_cent_pro_wh[stunde] if stunde < len(self.strompreis_cent_pro_wh) else self.strompreis_cent_pro_wh[-1]
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# for stunde in range(len(self.lastkurve_wh)):
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# verbrauch = self.lastkurve_wh[stunde]
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# erzeugung = self.pv_prognose_wh[stunde]
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# strompreis = self.strompreis_cent_pro_wh[stunde]
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stündlicher_netzbezug_wh = 0
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stündliche_kosten_euro = 0
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@ -2,54 +2,98 @@ 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 pprint import pprint
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import json, sys
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import json, sys, os
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import requests, hashlib
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class ForecastData:
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def __init__(self, date_time, dc_power, ac_power, windspeed_10m, temperature):
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self.date_time = date_time
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self.dc_power = dc_power
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self.ac_power = ac_power
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self.windspeed_10m = windspeed_10m
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self.temperature = temperature
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# Getter für die ForecastData-Attribute
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def get_date_time(self):
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return self.date_time
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def get_dc_power(self):
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return self.dc_power
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def get_ac_power(self):
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return self.ac_power
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def get_windspeed_10m(self):
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return self.windspeed_10m
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def get_temperature(self):
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return self.temperature
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class PVForecast:
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class ForecastData:
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def __init__(self, date_time, dc_power, ac_power, windspeed_10m, temperature):
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self.date_time = date_time
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self.dc_power = dc_power
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self.ac_power = ac_power
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self.windspeed_10m = windspeed_10m
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self.temperature = temperature
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# Getter für die ForecastData-Attribute
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def get_date_time(self):
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return self.date_time
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def get_dc_power(self):
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return self.dc_power
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def get_ac_power(self):
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return self.ac_power
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def get_windspeed_10m(self):
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return self.windspeed_10m
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def get_temperature(self):
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return self.temperature
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def __init__(self, filepath):
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self.filepath = filepath
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def __init__(self, filepath=None, url=None, cache_dir='cache'):
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self.meta = {}
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self.forecast_data = []
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self.load_data()
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self.cache_dir = cache_dir
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if not os.path.exists(self.cache_dir):
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os.makedirs(self.cache_dir)
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if filepath:
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self.load_data_from_file(filepath)
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elif url:
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self.load_data_with_caching(url)
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def load_data(self):
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with open(self.filepath, 'r') as file:
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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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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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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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# Erstelle eine ForecastData-Instanz für jeden Wert in der Liste
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forecast = self.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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self.process_data(data)
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def load_data_from_url(self, url):
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response = requests.get(url)
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if response.status_code == 200:
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data = response.json()
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pprint(data)
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self.process_data(data)
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else:
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print(f"Failed to load data from {url}. Status Code: {response.status_code}")
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self.load_data_from_url(url)
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def load_data_with_caching(self, url):
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cache_file = os.path.join(self.cache_dir, self.generate_cache_filename(url))
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if os.path.exists(cache_file):
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with open(cache_file, 'r') as file:
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data = json.load(file)
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print("Loading data from cache.")
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else:
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response = requests.get(url)
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if response.status_code == 200:
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data = response.json()
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with open(cache_file, 'w') as file:
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json.dump(data, file)
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print("Data fetched from URL and cached.")
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else:
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print(f"Failed to load data from {url}. Status Code: {response.status_code}")
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return
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self.process_data(data)
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def generate_cache_filename(self, url):
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# Erzeugt einen SHA-256 Hash der URL als Dateinamen
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hash_object = hashlib.sha256(url.encode())
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hex_dig = hash_object.hexdigest()
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return f"cache_{hex_dig}.json"
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def get_forecast_data(self):
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return self.forecast_data
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16
test.py
16
test.py
@ -16,7 +16,7 @@ import random
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import os
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date = "2024-02-16"
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date = "2024-02-26"
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akku_size = 1000 # Wh
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year_energy = 2000*1000 #Wh
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einspeiseverguetung_cent_pro_wh = np.full(24, 7/1000.0)
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@ -33,11 +33,13 @@ leistung_haushalt = lf.get_daily_stats(date)[0,...] # Datum anpassen
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pprint(leistung_haushalt.shape)
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# PV Forecast
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PVforecast = PVForecast(os.path.join(r'test_data', r'pvprognose.json'))
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#PVforecast = PVForecast(filepath=os.path.join(r'test_data', r'pvprognose.json'))
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PVforecast = PVForecast(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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pv_forecast = PVforecast.get_forecast_for_date(date)
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temperature_forecast = PVforecast.get_temperature_forecast_for_date(date)
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pprint(pv_forecast.shape)
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pprint(pv_forecast)
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sys.exit()
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# Strompreise
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filepath = os.path.join (r'test_data', r'strompreis.json') # Pfad zur JSON-Datei anpassen
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price_forecast = HourlyElectricityPriceForecast(filepath)
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@ -45,11 +47,11 @@ specific_date_prices = price_forecast.get_prices_for_date(date)
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# WP
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leistung_wp = wp.simulate_24h(temperature_forecast)
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# pprint(leistung_haushalt)
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# pprint(leistung_wp)
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# sys.exit()
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# LOAD
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load = leistung_haushalt + leistung_wp
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# EMS / Stromzähler Bilanz
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ems = EnergieManagementSystem(akku, load, pv_forecast, specific_date_prices, einspeiseverguetung_cent_pro_wh)
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o = ems.simuliere()
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