Modell funktioniert, Jahr 2019 eingestellt

.gitignore

@@ -1 +1,2 @@
 __pycache__/
+production_profile.csv

main.py

@@ -1,16 +1,15 @@
 from neighborhood import Producer, Consumer, Neighborhood
 
 # Anzahl Haushalte ohne PV-Anlagen
-num_consumer = 50
+num_consumer = 5
-avg_consumption_per_consumer = 10
 
 # Anzahl Haushalte mit PV-Anlagen
-num_producer = 10
+num_producer = 5
-avg_production_per_producer = 20
+
 
 # Instanzen für Erzeuger und Verbraucher anlegen
-consumer = Consumer(num_consumer, avg_consumption_per_consumer)
+producer = Producer(num_producer)
-producer = Producer(num_producer, avg_consumption_per_consumer, avg_production_per_producer)
+consumer = Consumer(num_consumer)
 
 # Instanz für Nachbarschaft anlegen und Ergebnis plotten
 neighborhood = Neighborhood(producer, consumer)

neighborhood.py

@@ -1,85 +1,88 @@
 import numpy as np
 import matplotlib.pyplot as plt
+import pv_input as pv
+import pandas as pd
 
-class Consumer:
-    def __init__(self, quantity, average_consumption):
-        self.quantity = quantity
-        self.consumption_profile = self.create_consumption_profile()
-        self.average_consumption = average_consumption
 
-    def create_consumption_profile(self):
-        profile = np.zeros(24)
-        peak_hours = [12]
-        for hour in peak_hours:
-            profile[hour] = 1.5
-        for hour in range(24):
-            if hour not in peak_hours:
-                profile[hour] = 0.8
-        return profile
-    
-    def calculate_daily_consumption(self):
-        daily_consumption = self.quantity * self.average_consumption * self.consumption_profile
-        return daily_consumption
 
 class Producer:
-    def __init__(self, quantity, average_consumption, average_production):
+    def __init__(self, quantity):
         self.quantity = quantity
         self.consumption_profile = self.create_consumption_profile()
-        self.average_consumption = average_consumption
         self.production_profile = self.create_production_profile()
-        self.average_production = average_production
+        self.final_consumption = self.calculate_final_consumption()
+        self.final_production = self.calculate_final_production()
 
+    # Vebrauchsprofil aus CSV-Datei in Dataframe einlesen und die Leistungs-Series extrahieren
     def create_consumption_profile(self):
-        profile = np.zeros(24)
+        consumption_profile = pd.read_csv('Lastprofil_final_H0.csv',delimiter=';')
-        peak_hours = [8,12,13,18,19,20,21]
+        return consumption_profile['Leistung']
-        for hour in peak_hours:
-            profile[hour] = 1.5
-        for hour in range(24):
-            if hour not in peak_hours:
-                profile[hour] = 0.5
-        return profile
     
+    # Verbrauchsprofil mit der Anzahl der Haushalte multiplizieren   
+    def calculate_final_consumption(self):
+        final_consumption = self.consumption_profile.mul(self.quantity)
+        return final_consumption
+    
+    # Erzegungsprofil über PV_Input.py erstelln, in .csv schreiben und von dort dann die Leistungs-Series extrahieren
     def create_production_profile(self):
-        profile = np.zeros(24)
+        production_profile = pv.create_production_profile()
-        peak_hours = [8,9,10,11,12,13,14,15,16,17,18,19]
+        production_profile.to_csv('production_profile.csv', sep=';',header=['Leistung'])
-        for hour in peak_hours:
+        production_profile = pd.read_csv('production_profile.csv',delimiter=';')
-            profile[hour] = 1.5
+        return production_profile['Leistung']
-        for hour in range(24):
+
-            if hour not in peak_hours:
+    # Erzeugungsprofil mit der Anzahl der Haushalte multiplizieren   
-                profile[hour] = 0
+    def calculate_final_production(self):
-        return profile
+        final_production = self.production_profile.mul(self.quantity)
+        return final_production
+    
+
+
+class Consumer:
+    def __init__(self, quantity):
+        self.quantity = quantity
+        self.consumption_profile = self.create_consumption_profile()
+        self.final_consumption = self.calculate_final_consumption()
+
+    # Vebrauchsprofil aus CSV-Datei in Dataframe einlesen und die Leistungs-Series extrahieren, Werte in Watt umrechnen und mit 4 multiplizieren, da Originalwerte 15-minütlich waren
+    def create_consumption_profile(self):
+        consumption_profile = pd.read_csv('Lastprofil_final_H0.csv',delimiter=';')
+        return consumption_profile['Leistung']*1000*4
+    
+    # Verbrauchsprofil mit der Anzahl der Haushalte multiplizieren
+    def calculate_final_consumption(self):
+        final_consumption = self.consumption_profile.mul(self.quantity)
+        return final_consumption
     
-    def calculate_daily_consumption(self):
-        daily_consumption = self.quantity * self.average_consumption * self.consumption_profile
-        return daily_consumption
 
-    def calculate_daily_production(self):
-        daily_production = self.quantity * self.average_production * self.production_profile
-        return daily_production
 
 class Neighborhood:
     def __init__(self, producer, consumer):
         self.producer = producer
         self.consumer = consumer
 
+    # Gesamterzeugung, Gesamtverbrauch und Nettoverbrauch plotten
     def plot_consumption(self):
-        total_consumption = -1*(self.consumer.calculate_daily_consumption() + self.producer.calculate_daily_consumption())
+        total_consumption = -1*(self.consumer.final_consumption + self.producer.final_consumption)
-        total_production = self.producer.calculate_daily_production()
+        total_production = self.producer.final_production
         net_value = total_consumption + total_production
 
-        # X-Werte anlegen
+        # X-Werte anlegen, einen Wert löschen da 8761 Werte vorhanden sind, aber nur 8760 benötigt werden
-        x = range(24)
+        x = pd.date_range(start='2018-12-31', end ='2019-12-31', freq='1h')
-        x_labels = ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24']
+        x = x[:-1]
         # Plot dimensionieren
         plt.figure(figsize=(10, 6))
         # y-Werte den x-Werten zuordnen
-        plt.plot(x, total_consumption, '--', x, total_production, '--', x, net_value, '-')
+        plt.bar(x, total_consumption)
+        plt.bar(x, total_production)
+        plt.bar(x, net_value)
+        #plt.plot(x, net_value, '-')
         # Titel und Achsenbeschriftungen anlegen
         plt.xlabel('Stunde')
         plt.ylabel('Strom (kWh)')
         plt.title('Produktion/Verbrauch Nachbarschaft')
-        # X-Werten die Labels zuordnen
+        # X-Ticks nur monatlich plotten
-        plt.xticks(x, x_labels)
+        monthly_ticks = pd.date_range(start='2018-12-31', end ='2019-12-31', freq='1MS')
+        plt.xticks(monthly_ticks)
         # Legende anlegen
         plt.legend(['Verbrauch', 'Produktion', 'Netto-Wert'], loc='upper right')
         # X-Labels rotieren

pv_input.py

@@ -39,44 +39,48 @@ surface_azimuth = 180
 # Temperaturparameter definieren
 temperature_parameters = TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_glass']
 
-# Location + PVSystem-Objekte anlegen und Modelchain damit füttern
+def create_production_profile(): 
-location = Location(latitude, longitude, tz)
+    # Location + PVSystem-Objekte anlegen und Modelchain damit füttern
-system = PVSystem(surface_tilt=surface_tilt, surface_azimuth=surface_azimuth, module_parameters=module, 
+    location = Location(latitude, longitude, tz)
-                  inverter_parameters=inverter, temperature_model_parameters=temperature_parameters, 
+    system = PVSystem(surface_tilt=surface_tilt, surface_azimuth=surface_azimuth, module_parameters=module, 
-                  modules_per_string=modules_per_string, strings_per_inverter=strings_per_inverter)
+                    inverter_parameters=inverter, temperature_model_parameters=temperature_parameters, 
+                    modules_per_string=modules_per_string, strings_per_inverter=strings_per_inverter)
 
-modelchain = ModelChain(system, location)
+    modelchain = ModelChain(system, location)
 
-# Ertragssimulation mit Clear-Sky Modell
+    # Ertragssimulation mit Clear-Sky Modell
 
-# Index-Spalte mit Zeiten für clear-sky Dataset anlegen
+    # Index-Spalte mit Zeiten für clear-sky Dataset anlegen
-# times = pd.date_range(start='2020-01-01', end ='2020-12-31', freq='1h', tz=location.tz)
+    # times = pd.date_range(start='2020-01-01', end ='2020-12-31', freq='1h', tz=location.tz)
 
-# clear_sky = location.get_clearsky(times)
+    # clear_sky = location.get_clearsky(times)
-# #clear_sky.plot(figsize=(16,9))
+    # #clear_sky.plot(figsize=(16,9))
 
-# modelchain.run_model(clear_sky)
+    # modelchain.run_model(clear_sky)
 
-# Ertragssimulation mit realen Strahlungsdaten aus Wetterjahr
+    # Ertragssimulation mit realen Strahlungsdaten aus Wetterjahr
 
-# Hier ist Süden Azimuth = 0°, bei PVLib ist es 180°
+    # Hier ist Süden Azimuth = 0°, bei PVLib ist es 180°
-poa_data, meta, inputs = pvlib.iotools.get_pvgis_hourly(latitude=latitude, longitude=longitude, start=year, end=year, raddatabase='PVGIS-SARAH3', components=True, surface_tilt=surface_tilt, 
+    poa_data, meta, inputs = pvlib.iotools.get_pvgis_hourly(latitude=latitude, longitude=longitude, start=year, end=year, raddatabase='PVGIS-SARAH3', components=True, surface_tilt=surface_tilt, 
-                               surface_azimuth=surface_azimuth-180, outputformat='json', usehorizon=True, userhorizon=None, pvcalculation=False, peakpower=None, 
+                                surface_azimuth=surface_azimuth-180, outputformat='json', usehorizon=True, userhorizon=None, pvcalculation=False, peakpower=None, 
-                               pvtechchoice='crystSi', mountingplace='free', loss=0, trackingtype=0, optimal_surface_tilt=False, optimalangles=False, 
+                                pvtechchoice='crystSi', mountingplace='free', loss=0, trackingtype=0, optimal_surface_tilt=False, optimalangles=False, 
-                               url='https://re.jrc.ec.europa.eu/api/', map_variables=True, timeout=30)
+                                url='https://re.jrc.ec.europa.eu/api/', map_variables=True, timeout=30)
 
-# Notwendige Spalten ausrechnen und hinzufügen, sodass Modelchain sie verwenden kann
+    # Notwendige Spalten ausrechnen und hinzufügen, sodass Modelchain sie verwenden kann
-poa_data['poa_diffuse'] = poa_data['poa_sky_diffuse'] + poa_data['poa_ground_diffuse']
+    poa_data['poa_diffuse'] = poa_data['poa_sky_diffuse'] + poa_data['poa_ground_diffuse']
-poa_data['poa_global'] = poa_data['poa_diffuse'] + poa_data['poa_direct']
+    poa_data['poa_global'] = poa_data['poa_diffuse'] + poa_data['poa_direct']
 
-# Daten in csv exportieren
+    # Daten in csv exportieren
-#poa_data.to_csv('poa_data.csv') 
+    #poa_data.to_csv('poa_data.csv') 
 
-# Index des Dataframe mit datetime-Index von Pandas überschreiben
+    # Index des Dataframe mit datetime-Index von Pandas überschreiben
-poa_data.index = pd.to_datetime((poa_data.index))
+    poa_data.index = pd.to_datetime((poa_data.index))
-# Diese Funktion benötigt POA-Daten anstatt g(i)-Daten -> DOKU
+    # Diese Funktion benötigt POA-Daten anstatt g(i)-Daten -> DOKU
-modelchain.run_model_from_poa(poa_data)
+    modelchain.run_model_from_poa(poa_data)
 
-# Ergebnis plotten
+    # Ergebnis plotten
-modelchain.results.ac.plot(figsize=(16,9))     
+    #modelchain.results.ac.plot(figsize=(16,9))    
-plt.show()
+    #plt.show()
+
+    # Rückgabewert
+    return modelchain.results.ac