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Strompreise aus URL möglich, Source unsere API + pers. Cache
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commit
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@ -1,82 +1,150 @@
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import json
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from datetime import datetime, timedelta, timezone
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import numpy as np
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import json, os
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from datetime import datetime
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import hashlib, requests
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class HourlyElectricityPriceForecast:
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class PriceData:
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def __init__(self, total, energy, tax, starts_at, currency, level):
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self.total = total/1000.0
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self.energy = energy/1000.0
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self.tax = tax/1000.0
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self.starts_at = datetime.strptime(starts_at, '%Y-%m-%dT%H:%M:%S.%f%z')
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def __init__(self, source, cache_dir='cache'):
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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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self.prices = self.load_data(source)
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self.currency = currency
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self.level = level
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# Getter-Methoden
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def get_total(self):
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return self.total
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def get_energy(self):
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return self.energy
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def get_tax(self):
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return self.tax
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def get_starts_at(self):
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return self.starts_at
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def get_currency(self):
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return self.currency
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def get_level(self):
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return self.level
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def __init__(self, filepath):
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self.filepath = filepath
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self.price_data = []
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self.load_data()
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def get_prices_for_date(self, query_date):
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query_date = datetime.strptime(query_date, '%Y-%m-%d').date()
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prices_for_date = [price.get_total() for price in self.price_data if price.starts_at.date() == query_date]
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return np.array(prices_for_date)
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def get_price_for_datetime(self, query_datetime):
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query_datetime = datetime.strptime(query_datetime, '%Y-%m-%d %H').replace(minute=0, second=0, microsecond=0)
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query_datetime = query_datetime.replace(tzinfo=timezone(timedelta(hours=1)))
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for price in self.price_data:
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#print(price.starts_at.replace(minute=0, second=0, microsecond=0) , " ", query_datetime, " == ",price.starts_at.replace(minute=0, second=0, microsecond=0) == query_datetime)
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if price.starts_at.replace(minute=0, second=0, microsecond=0) == query_datetime:
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return np.array(price)
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return None
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def load_data(self):
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with open(self.filepath, 'r') as file:
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def load_data(self, source):
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if source.startswith('http'):
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cache_filename = self.get_cache_filename(source)
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if os.path.exists(cache_filename):
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print("Lade Daten aus dem Cache...")
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with open(cache_filename, 'r') as file:
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data = json.load(file)
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for item in data['payload']:
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self.price_data.append(self.PriceData(
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total=item['total'],
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energy=item['energy'],
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tax=item['tax'],
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starts_at=item['startsAt'],
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currency=item['currency'],
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level=item['level']
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))
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else:
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print("Lade Daten von der URL...")
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response = requests.get(source)
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if response.status_code == 200:
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data = response.json()
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with open(cache_filename, 'w') as file:
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json.dump(data, file)
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else:
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raise Exception(f"Fehler beim Abrufen der Daten: {response.status_code}")
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else:
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with open(source, 'r') as file:
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data = json.load(file)
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return data['values']
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def get_price_data(self):
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return self.price_data
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def get_cache_filename(self, url):
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hash_object = hashlib.sha256(url.encode())
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hex_dig = hash_object.hexdigest()
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return os.path.join(self.cache_dir, f"cache_{hex_dig}.json")
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# Beispiel für die Verwendung der Klasse
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if __name__ == '__main__':
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filepath = r'..\test_data\strompreis.json' # Pfad zur JSON-Datei anpassen
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price_forecast = HourlyElectricityPriceForecast(filepath)
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specific_date_prices = price_forecast.get_prices_for_date('2024-02-16') # Datum anpassen
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specific_date_prices = price_forecast.get_price_for_datetime('2024-02-16 12')
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print(specific_date_prices)
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#for price in price_forecast.get_price_data():
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# print(price.get_starts_at(), price.get_total(), price.get_currency())
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def get_price_for_date(self, date_str):
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"""Gibt alle Preise für das spezifizierte Datum zurück."""
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date_prices = [entry["marketpriceEurocentPerKWh"] for entry in self.prices if date_str in entry['start']]
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return date_prices
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# def get_price_for_hour(self, datetime_str):
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# """Gibt den Preis für die spezifizierte Stunde zurück."""
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# hour_price = [entry for entry in self.prices if datetime_str in entry['start']]
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# return hour_price[0] if hour_price else None
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# # Beispiel zur Verwendung der Klasse
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# filepath = '/mnt/data/strompreise_akkudokAPI.json' # Pfad zur JSON-Datei
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# strompreise = Strompreise(filepath)
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# # Preise für ein spezifisches Datum erhalten
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# date_str = '2024-02-25'
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# prices_for_date = strompreise.get_price_for_date(date_str)
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# print(f"Preise für {date_str}: {prices_for_date}")
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# # Preis für eine spezifische Stunde erhalten
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# datetime_str = '2024-02-25T15:00:00.000Z'
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# price_for_hour = strompreise.get_price_for_hour(datetime_str)
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# print(f"Preis für {datetime_str}: {price_for_hour}")
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# class HourlyElectricityPriceForecast:
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# class PriceData:
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# def __init__(self, total, energy, tax, starts_at, currency, level):
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# self.total = total/1000.0
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# self.energy = energy/1000.0
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# self.tax = tax/1000.0
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# self.starts_at = datetime.strptime(starts_at, '%Y-%m-%dT%H:%M:%S.%f%z')
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# self.currency = currency
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# self.level = level
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# # Getter-Methoden
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# def get_total(self):
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# return self.total
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# def get_energy(self):
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# return self.energy
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# def get_tax(self):
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# return self.tax
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# def get_starts_at(self):
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# return self.starts_at
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# def get_currency(self):
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# return self.currency
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# def get_level(self):
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# return self.level
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# def __init__(self, filepath):
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# self.filepath = filepath
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# self.price_data = []
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# self.load_data()
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# def get_prices_for_date(self, query_date):
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# query_date = datetime.strptime(query_date, '%Y-%m-%d').date()
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# prices_for_date = [price.get_total() for price in self.price_data if price.starts_at.date() == query_date]
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# return np.array(prices_for_date)
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# def get_price_for_datetime(self, query_datetime):
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# query_datetime = datetime.strptime(query_datetime, '%Y-%m-%d %H').replace(minute=0, second=0, microsecond=0)
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# query_datetime = query_datetime.replace(tzinfo=timezone(timedelta(hours=1)))
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# for price in self.price_data:
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# #print(price.starts_at.replace(minute=0, second=0, microsecond=0) , " ", query_datetime, " == ",price.starts_at.replace(minute=0, second=0, microsecond=0) == query_datetime)
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# if price.starts_at.replace(minute=0, second=0, microsecond=0) == query_datetime:
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# return np.array(price)
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# return None
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# def load_data(self):
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# with open(self.filepath, 'r') as file:
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# data = json.load(file)
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# for item in data['payload']:
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# self.price_data.append(self.PriceData(
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# total=item['total'],
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# energy=item['energy'],
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# tax=item['tax'],
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# starts_at=item['startsAt'],
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# currency=item['currency'],
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# level=item['level']
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# ))
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# def get_price_data(self):
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# return self.price_data
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# # Beispiel für die Verwendung der Klasse
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# if __name__ == '__main__':
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# filepath = r'..\test_data\strompreis.json' # Pfad zur JSON-Datei anpassen
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# price_forecast = HourlyElectricityPriceForecast(filepath)
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# specific_date_prices = price_forecast.get_prices_for_date('2024-02-16') # Datum anpassen
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# specific_date_prices = price_forecast.get_price_for_datetime('2024-02-16 12')
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# print(specific_date_prices)
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# #for price in price_forecast.get_price_data():
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# # print(price.get_starts_at(), price.get_total(), price.get_currency())
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22
test.py
22
test.py
@ -28,22 +28,32 @@ akku = PVAkku(akku_size)
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discharge_array = np.full(24,1)
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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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pprint(leistung_haushalt.shape)
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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(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)
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sys.exit()
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pprint(pv_forecast.shape)
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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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specific_date_prices = price_forecast.get_prices_for_date(date)
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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+"&end="+date+"")
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specific_date_prices = price_forecast.get_price_for_date(date)
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pprint(f"Preise für {date}: {specific_date_prices}")
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# WP
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leistung_wp = wp.simulate_24h(temperature_forecast)
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