PV Forecast mit URL möglich + persistenter Cache eingebaut

modules/class_ems.py

@@ -45,7 +45,7 @@ class EnergieManagementSystem:
     def reset(self):
         self.akku.reset()
         
-    def simuliere(self):
+    def simuliere(self, start_stunde):
         eigenverbrauch_wh_pro_stunde = []
         netzeinspeisung_wh_pro_stunde = []
         netzbezug_wh_pro_stunde = []
@@ -53,10 +53,18 @@ class EnergieManagementSystem:
         einnahmen_euro_pro_stunde = []
         akku_soc_pro_stunde = []
 
-        for stunde in range(len(self.lastkurve_wh)):
+        ende = len(self.lastkurve_wh)  # Berechnet das Ende basierend auf der Länge der Lastkurve
+        for stunde in range(start_stunde, ende):
+            # Anpassung, um sicherzustellen, dass Indizes korrekt sind
             verbrauch = self.lastkurve_wh[stunde]
             erzeugung = self.pv_prognose_wh[stunde]
-            strompreis = self.strompreis_cent_pro_wh[stunde]
+            strompreis = self.strompreis_cent_pro_wh[stunde] if stunde < len(self.strompreis_cent_pro_wh) else self.strompreis_cent_pro_wh[-1]
+
+
+        # for stunde in range(len(self.lastkurve_wh)):
+            # verbrauch = self.lastkurve_wh[stunde]
+            # erzeugung = self.pv_prognose_wh[stunde]
+            # strompreis = self.strompreis_cent_pro_wh[stunde]
 
             stündlicher_netzbezug_wh = 0
             stündliche_kosten_euro = 0

modules/class_pv_forecast.py

@@ -2,54 +2,98 @@ from flask import Flask, jsonify, request
 import numpy as np
 from datetime import datetime
 from pprint import pprint
-import json, sys
+import json, sys, os
+import requests, hashlib
+
+
+class ForecastData:
+    def __init__(self, date_time, dc_power, ac_power, windspeed_10m, temperature):
+        self.date_time = date_time
+        self.dc_power = dc_power
+        self.ac_power = ac_power
+        self.windspeed_10m = windspeed_10m
+        self.temperature = temperature
+
+    # Getter für die ForecastData-Attribute
+    def get_date_time(self):
+        return self.date_time
+
+    def get_dc_power(self):
+        return self.dc_power
+
+    def get_ac_power(self):
+        return self.ac_power
+
+    def get_windspeed_10m(self):
+        return self.windspeed_10m
+
+    def get_temperature(self):
+        return self.temperature
 
 class PVForecast:
-    class ForecastData:
+    def __init__(self, filepath=None, url=None, cache_dir='cache'):
-        def __init__(self, date_time, dc_power, ac_power, windspeed_10m, temperature):
-            self.date_time = date_time
-            self.dc_power = dc_power
-            self.ac_power = ac_power
-            self.windspeed_10m = windspeed_10m
-            self.temperature = temperature
-
-        # Getter für die ForecastData-Attribute
-        def get_date_time(self):
-            return self.date_time
-
-        def get_dc_power(self):
-            return self.dc_power
-
-        def get_ac_power(self):
-            return self.ac_power
-
-        def get_windspeed_10m(self):
-            return self.windspeed_10m
-
-        def get_temperature(self):
-            return self.temperature
-
-    def __init__(self, filepath):
-        self.filepath = filepath
         self.meta = {}
         self.forecast_data = []
-        self.load_data()
+        self.cache_dir = cache_dir
+        if not os.path.exists(self.cache_dir):
+            os.makedirs(self.cache_dir)
+        if filepath:
+            self.load_data_from_file(filepath)
+        elif url:
+            self.load_data_with_caching(url)
 
-    def load_data(self):
+
-        with open(self.filepath, 'r') as file:
+    def process_data(self, data):
+        self.meta = data.get('meta', {})
+        values = data.get('values', [])[0]
+        for value in values:
+            forecast = ForecastData(
+                date_time=value.get('datetime'),
+                dc_power=value.get('dcPower'),
+                ac_power=value.get('power'),
+                windspeed_10m=value.get('windspeed_10m'),
+                temperature=value.get('temperature')
+            )
+            self.forecast_data.append(forecast)
+
+    def load_data_from_file(self, filepath):
+        with open(filepath, 'r') as file:
             data = json.load(file)
-            self.meta = data.get('meta', {})
+            self.process_data(data)
-            values = data.get('values', [])[0]
+
-            for value in values:
+    def load_data_from_url(self, url):
-                # Erstelle eine ForecastData-Instanz für jeden Wert in der Liste
+        response = requests.get(url)
-                forecast = self.ForecastData(
+        if response.status_code == 200:
-                    date_time=value.get('datetime'),
+            data = response.json()
-                    dc_power=value.get('dcPower'),
+            pprint(data)
-                    ac_power=value.get('power'),
+            self.process_data(data)
-                    windspeed_10m=value.get('windspeed_10m'),
+        else:
-                    temperature=value.get('temperature')
+            print(f"Failed to load data from {url}. Status Code: {response.status_code}")
-                )
+            self.load_data_from_url(url)
-                self.forecast_data.append(forecast)
+
+    def load_data_with_caching(self, url):
+        cache_file = os.path.join(self.cache_dir, self.generate_cache_filename(url))
+        if os.path.exists(cache_file):
+            with open(cache_file, 'r') as file:
+                data = json.load(file)
+                print("Loading data from cache.")
+        else:
+            response = requests.get(url)
+            if response.status_code == 200:
+                data = response.json()
+                with open(cache_file, 'w') as file:
+                    json.dump(data, file)
+                print("Data fetched from URL and cached.")
+            else:
+                print(f"Failed to load data from {url}. Status Code: {response.status_code}")
+                return
+        self.process_data(data)
+
+    def generate_cache_filename(self, url):
+        # Erzeugt einen SHA-256 Hash der URL als Dateinamen
+        hash_object = hashlib.sha256(url.encode())
+        hex_dig = hash_object.hexdigest()
+        return f"cache_{hex_dig}.json"
 
     def get_forecast_data(self):
         return self.forecast_data

test.py

@@ -16,7 +16,7 @@ import random
 import os
 
 
-date = "2024-02-16"
+date = "2024-02-26"
 akku_size = 1000 # Wh
 year_energy = 2000*1000 #Wh
 einspeiseverguetung_cent_pro_wh = np.full(24, 7/1000.0)
@@ -33,11 +33,13 @@ leistung_haushalt = lf.get_daily_stats(date)[0,...]  # Datum anpassen
 pprint(leistung_haushalt.shape)
 
 # PV Forecast
-PVforecast = PVForecast(os.path.join(r'test_data', r'pvprognose.json'))
+#PVforecast = PVForecast(filepath=os.path.join(r'test_data', r'pvprognose.json'))
+
+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")
 pv_forecast = PVforecast.get_forecast_for_date(date)
 temperature_forecast = PVforecast.get_temperature_forecast_for_date(date)
-pprint(pv_forecast.shape)
+pprint(pv_forecast)
-
+sys.exit()
 # Strompreise
 filepath = os.path.join (r'test_data', r'strompreis.json')  # Pfad zur JSON-Datei anpassen
 price_forecast = HourlyElectricityPriceForecast(filepath)
@@ -45,11 +47,11 @@ specific_date_prices = price_forecast.get_prices_for_date(date)
 
 # WP
 leistung_wp = wp.simulate_24h(temperature_forecast)
-# pprint(leistung_haushalt)
+
-# pprint(leistung_wp)
+# LOAD
-# sys.exit()
 load = leistung_haushalt + leistung_wp
 
+
 # EMS / Stromzähler Bilanz
 ems = EnergieManagementSystem(akku, load, pv_forecast, specific_date_prices, einspeiseverguetung_cent_pro_wh)
 o = ems.simuliere()