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Wallbox Leistung wird von der Lastprognose abgezogen
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Bla Bla
@@ -1,23 +1,22 @@
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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 datetime import datetime, timedelta
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from pprint import pprint
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import json, sys, os
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import requests, hashlib
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from dateutil import parser, tz
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from dateutil import parser
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import pandas as pd
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class ForecastData:
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def __init__(self, date_time, dc_power, ac_power, windspeed_10m=None, temperature=None,ac_power_measurement=None):
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def __init__(self, date_time, dc_power, ac_power, windspeed_10m=None, temperature=None, ac_power_measurement=None):
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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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self.ac_power_measurement = None
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# Getter für die ForecastData-Attribute
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self.ac_power_measurement = ac_power_measurement
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def get_date_time(self):
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return self.date_time
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@@ -28,7 +27,7 @@ class ForecastData:
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return self.ac_power_measurement
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def get_ac_power(self):
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if self.ac_power_measurement != None:
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if self.ac_power_measurement is not None:
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return self.ac_power_measurement
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else:
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return self.ac_power
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@@ -40,73 +39,63 @@ class ForecastData:
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return self.temperature
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class PVForecast:
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def __init__(self, filepath=None, url=None, cache_dir='cache', prediction_hours = 48):
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def __init__(self, filepath=None, url=None, cache_dir='cache', prediction_hours=48):
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self.meta = {}
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self.forecast_data = []
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self.cache_dir = cache_dir
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self.prediction_hours = prediction_hours
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self.current_measurement = None
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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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# Überprüfung nach dem Laden der Daten
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if len(self.forecast_data) < self.prediction_hours:
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raise ValueError(f"Die Vorhersage muss mindestens {self.prediction_hours} Stunden umfassen, aber es wurden nur {len(self.forecast_data)} Stunden vorhergesagt.")
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def update_ac_power_measurement(self, date_time=None, ac_power_measurement=None):
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"""Aktualisiert einen DC-Leistungsmesswert oder fügt ihn hinzu."""
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found = False
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target_timezone = tz.gettz('Europe/Berlin')
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input_date_hour = date_time.astimezone(target_timezone).replace(minute=0, second=0, microsecond=0)
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input_date_hour = date_time.replace(minute=0, second=0, microsecond=0)
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for forecast in self.forecast_data:
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forecast_date_hour = datetime.strptime(forecast.date_time, "%Y-%m-%dT%H:%M:%S.%f%z").astimezone(target_timezone).replace(minute=0, second=0, microsecond=0)
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#print(forecast_date_hour," ",input_date_hour)
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forecast_date_hour = parser.parse(forecast.date_time).replace(minute=0, second=0, microsecond=0)
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if forecast_date_hour == input_date_hour:
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forecast.ac_power_measurement = ac_power_measurement
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found = True
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break
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# if not found:
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# # Erstelle ein neues ForecastData-Objekt, falls kein entsprechender Zeitstempel gefunden wurde
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# # Hier kannst du entscheiden, wie die anderen Werte gesetzt werden sollen, falls keine Vorhersage existiert
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# new_forecast = ForecastData(date_time, dc_power=None, ac_power=None, dc_power_measurement=dc_power_measurement)
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# self.forecast_data.append(new_forecast)
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# # Liste sortieren, um sie chronologisch zu ordnen
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# self.forecast_data.sort(key=lambda x: datetime.strptime(x.date_time, "%Y-%m-%dT%H:%M:%S.%f%z").replace(minute=0, second=0, microsecond=0))
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def process_data(self, data):
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self.meta = data.get('meta', {})
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all_values = data.get('values', [])
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# Berechnung der Summe der DC- und AC-Leistungen für jeden Zeitstempel
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for i in range(len(all_values[0])): # Annahme, dass alle Listen gleich lang sind
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sum_dc_power = sum(values[i]['dcPower'] for values in all_values)
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sum_ac_power = sum(values[i]['power'] for values in all_values)
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# Erstellen eines ForecastData-Objekts mit den summierten Werten
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# Zeige die ursprünglichen und berechneten Zeitstempel an
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original_datetime = all_values[0][i].get('datetime')
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#print(original_datetime," ",sum_dc_power," ",all_values[0][i]['dcPower'])
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dt = datetime.strptime(original_datetime, "%Y-%m-%dT%H:%M:%S.%f%z")
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dt = dt.replace(tzinfo=None)
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#iso_datetime = parser.parse(original_datetime).isoformat() # Konvertiere zu ISO-Format
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#print()
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# Optional: 2 Stunden abziehen, um die Zeitanpassung zu testen
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#adjusted_datetime = parser.parse(original_datetime) - timedelta(hours=2)
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#print(f"Angepasste Zeitstempel: {adjusted_datetime.isoformat()}")
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forecast = ForecastData(
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date_time=all_values[0][i].get('datetime'),
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date_time=dt, # Verwende angepassten Zeitstempel
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dc_power=sum_dc_power,
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ac_power=sum_ac_power,
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# Optional: Weitere Werte wie Windspeed und Temperature, falls benötigt
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windspeed_10m=all_values[0][i].get('windspeed_10m'),
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temperature=all_values[0][i].get('temperature')
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)
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self.forecast_data.append(forecast)
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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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@@ -124,9 +113,9 @@ class PVForecast:
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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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date = datetime.now().strftime("%Y-%m-%d")
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date = datetime.now().strftime("%Y-%m-%d")
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cache_file = os.path.join(self.cache_dir, self.generate_cache_filename(url,date))
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cache_file = os.path.join(self.cache_dir, self.generate_cache_filename(url, date))
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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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@@ -143,28 +132,16 @@ class PVForecast:
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return
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self.process_data(data)
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def generate_cache_filename(self, url,date):
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# Erzeugt einen SHA-256 Hash der URL als Dateinamen
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def generate_cache_filename(self, url, date):
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cache_key = hashlib.sha256(f"{url}{date}".encode('utf-8')).hexdigest()
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#cache_path = os.path.join(self.cache_dir, cache_key)
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return f"cache_{cache_key}.json"
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def get_forecast_data(self):
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return self.forecast_data
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# def get_forecast_for_date(self, input_date_str):
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# input_date = datetime.strptime(input_date_str, "%Y-%m-%d")
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# daily_forecast_obj = [data for data in self.forecast_data if datetime.strptime(data.get_date_time(), "%Y-%m-%dT%H:%M:%S.%f%z").date() == input_date.date()]
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# daily_forecast = []
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# for d in daily_forecast_obj:
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# daily_forecast.append(d.get_ac_power())
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# return np.array(daily_forecast)
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def get_temperature_forecast_for_date(self, input_date_str):
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input_date = datetime.strptime(input_date_str, "%Y-%m-%d")
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daily_forecast_obj = [data for data in self.forecast_data if datetime.strptime(data.get_date_time(), "%Y-%m-%dT%H:%M:%S.%f%z").date() == input_date.date()]
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daily_forecast_obj = [data for data in self.forecast_data if parser.parse(data.get_date_time()).date() == input_date.date()]
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daily_forecast = []
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for d in daily_forecast_obj:
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daily_forecast.append(d.get_temperature())
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@@ -177,10 +154,10 @@ class PVForecast:
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date_range_forecast = []
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for data in self.forecast_data:
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data_date = datetime.strptime(data.get_date_time(), "%Y-%m-%dT%H:%M:%S.%f%z").date()
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#print(data.get_date_time())
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data_date = data.get_date_time().date()#parser.parse(data.get_date_time()).date()
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if start_date <= data_date <= end_date:
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date_range_forecast.append(data)
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print(data.get_date_time()," ",data.get_ac_power())
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ac_power_forecast = np.array([data.get_ac_power() for data in date_range_forecast])
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@@ -192,28 +169,36 @@ class PVForecast:
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date_range_forecast = []
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for data in self.forecast_data:
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data_date = datetime.strptime(data.get_date_time(), "%Y-%m-%dT%H:%M:%S.%f%z").date()
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data_date = data.get_date_time().date()
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if start_date <= data_date <= end_date:
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date_range_forecast.append(data)
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forecast_data = date_range_forecast
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temperature_forecast = [data.get_temperature() for data in forecast_data]
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temperature_forecast = [data.get_temperature() for data in date_range_forecast]
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return np.array(temperature_forecast)[:self.prediction_hours]
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def get_forecast_dataframe(self):
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# Wandelt die Vorhersagedaten in ein Pandas DataFrame um
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data = [{
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'date_time': f.get_date_time(),
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'dc_power': f.get_dc_power(),
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'ac_power': f.get_ac_power(),
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'windspeed_10m': f.get_windspeed_10m(),
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'temperature': f.get_temperature()
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} for f in self.forecast_data]
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# Erstelle ein DataFrame
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df = pd.DataFrame(data)
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return df
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def print_ac_power_and_measurement(self):
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"""Druckt die DC-Leistung und das Messwert für jede Stunde."""
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"""Druckt die DC-Leistung und den Messwert für jede Stunde."""
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for forecast in self.forecast_data:
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date_time = forecast.date_time
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print(f"Zeit: {date_time}, DC: {forecast.dc_power}, AC: {forecast.ac_power}, Messwert: {forecast.ac_power_measurement} AC GET: {forecast.get_ac_power()}")
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print(f"Zeit: {date_time}, DC: {forecast.dc_power}, AC: {forecast.ac_power}, Messwert: {forecast.ac_power_measurement}, AC GET: {forecast.get_ac_power()}")
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# Beispiel für die Verwendung der Klasse
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if __name__ == '__main__':
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date_now = datetime.now()
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forecast = PVForecast(prediction_hours = 24, 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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forecast = PVForecast(prediction_hours=24, 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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forecast.update_ac_power_measurement(date_time=datetime.now(), ac_power_measurement=1000)
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forecast.print_ac_power_and_measurement()
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