Lastprofile angepasst, variable Zahl an Producer+Consumer

main.py

@@ -1,16 +1,17 @@
 from neighborhood import Producer, Consumer, Neighborhood
 
-# Anzahl Haushalte ohne PV-Anlagen
+# Consumer-Instanzen anlegen
-num_consumer = 5
+standard_consumer = Consumer(quantity=10, profile_type='std')
-
+wp_consumer = Consumer(quantity=0, profile_type='wp')
-# Anzahl Haushalte mit PV-Anlagen
+ea_consumer = Consumer(quantity=0, profile_type='ea')
-num_producer = 5
 
 
-# Instanzen für Erzeuger und Verbraucher anlegen
+# Producer-Instanz anlegen
-producer = Producer(num_producer)
+standard_producer = Producer(quantity=0, profile_type='std')
-consumer = Consumer(num_consumer)
+wp_producer = Producer(quantity=0, profile_type='wp')
+ea_producer = Producer(quantity=0, profile_type='ea')
+
 
 # Instanz für Nachbarschaft anlegen und Ergebnis plotten
-neighborhood = Neighborhood(producer, consumer)
+neighborhood = Neighborhood(producers=[standard_producer, wp_producer, ea_producer], consumers=[standard_consumer, wp_consumer, ea_consumer])
 neighborhood.plot_consumption()

neighborhood.py

@@ -1,26 +1,30 @@
-# https://stackoverflow.com/questions/63511090/how-can-i-smooth-data-in-python
 import numpy as np
 import matplotlib.pyplot as plt
 import pv_input as pv
 import pandas as pd
-from scipy.signal import savgol_filter   
-
-
 
 class Producer:
-    def __init__(self, quantity):
+    def __init__(self, quantity, profile_type):
         self.quantity = quantity
+        self.profile_type = profile_type
         self.consumption_profile = self.create_consumption_profile()
         self.production_profile = self.create_production_profile()
         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
+    # Vebrauchsprofile aus CSV-Datei in Dataframe einlesen und je nach profile_type die entsprechende Leistungs-Series extrahieren
     def create_consumption_profile(self):
-        consumption_profile = pd.read_csv('Lastprofil_final_H0.csv',delimiter=';')
+        if self.profile_type == 'std':
-        return consumption_profile['Leistung']
+            consumption_profile = pd.read_csv('Lastprofile_gesamt.csv',delimiter=';')
+            return consumption_profile['Leistung']*1000
+        elif self.profile_type == 'wp':
+            consumption_profile = pd.read_csv('Lastprofile_gesamt.csv',delimiter=';')
+            return consumption_profile['Leistung_WP']*1000
+        elif self.profile_type == 'ea':
+            consumption_profile = pd.read_csv('Lastprofile_gesamt.csv',delimiter=';')
+            return consumption_profile['Leistung_EA']*1000
     
-    # Verbrauchsprofil mit der Anzahl der Haushalte multiplizieren   
+    # Verbrauchsprofil mit der Anzahl der entsprechenden Haushalte multiplizieren   
     def calculate_final_consumption(self):
         final_consumption = self.consumption_profile.mul(self.quantity)
         return final_consumption
@@ -32,7 +36,7 @@ class Producer:
         production_profile = pd.read_csv('production_profile.csv',delimiter=';')
         return production_profile['Leistung']
 
-    # Erzeugungsprofil mit der Anzahl der Haushalte multiplizieren   
+    # Erzeugungsprofil mit der Anzahl der entsprechenden Haushalte multiplizieren   
     def calculate_final_production(self):
         final_production = self.production_profile.mul(self.quantity)
         return final_production
@@ -40,17 +44,26 @@ class Producer:
 
 
 class Consumer:
-    def __init__(self, quantity):
+    def __init__(self, quantity, profile_type):
         self.quantity = quantity
+        self.profile_type = profile_type
         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 
+    # Vebrauchsprofile aus CSV-Datei in Dataframe einlesen und je nach profile_type die entsprechende Leistungs-Series extrahieren
     def create_consumption_profile(self):
-        consumption_profile = pd.read_csv('Lastprofile_gesamt.csv',delimiter=';')
+        if self.profile_type == 'std':
-        return consumption_profile['Leistung']*1000
+            consumption_profile = pd.read_csv('Lastprofile_gesamt.csv',delimiter=';')
+            return consumption_profile['Leistung']*1000
+        elif self.profile_type == 'wp':
+            consumption_profile = pd.read_csv('Lastprofile_gesamt.csv',delimiter=';')
+            return consumption_profile['Leistung_WP']*1000
+        elif self.profile_type == 'ea':
+            consumption_profile = pd.read_csv('Lastprofile_gesamt.csv',delimiter=';')
+            return consumption_profile['Leistung_EA']*1000
 
-    # Verbrauchsprofil mit der Anzahl der Haushalte multiplizieren
+    
+    # Verbrauchsprofil mit der Anzahl der entsprechenden Haushalte multiplizieren
     def calculate_final_consumption(self):
         final_consumption = self.consumption_profile.mul(self.quantity)
         return final_consumption
@@ -58,17 +71,28 @@ class Consumer:
 
 
 class Neighborhood:
-    def __init__(self, producer, consumer):
+    def __init__(self, producers, consumers):
-        self.producer = producer
+        self.producers = producers if isinstance(producers, list) else [producers]
-        self.consumer = consumer
+        self.consumers = consumers if isinstance(consumers, list) else [consumers]
 
     # Gesamterzeugung, Gesamtverbrauch und Nettoverbrauch plotten
     def plot_consumption(self):
-        total_consumption = -1*(self.consumer.final_consumption + self.producer.final_consumption)
+        total_consumption = 0
-        total_production = self.producer.final_production
+        total_production = 0
+        # Verbrauch der Consumer subtrahieren
+        for consumer in self.consumers:
+            total_consumption -= consumer.final_consumption
+        # Zusätzlich Verbrauch der Producer subtrahieren
+        for producer in self.producers:
+            total_consumption -= producer.final_consumption
+        # Produktion der Producer addieren
+        for producer in self.producers:
+            total_production += producer.final_production
+        # Netto-Erzeugnis ausrechnen
         net_value = total_consumption + total_production
-        net_value_mean = net_value.rolling(168).mean()
+        # Rollendes Mittel des netto-Erzeugnisses ausrechnen
-        net_value_filtered = net_value.apply(savgol_filter, window_length=168, polyorder=2)
+        net_value_mean = net_value.rolling(24).mean()
+        
         
         # X-Werte anlegen, einen Wert löschen da 8761 Werte vorhanden sind, aber nur 8760 benötigt werden
         x = pd.date_range(start='2018-12-31', end ='2019-12-31', freq='1h')
@@ -77,33 +101,34 @@ class Neighborhood:
         # Plot dimensionieren
         plt.figure(figsize=(15, 9))
 
-        # Barplot anlegen
+        # Barplots anlegen
+        plt.bar(x, total_consumption)
+        plt.bar(x, total_production,color='orange')
            
-        #plt.bar(x, total_consumption)
+        # Linienplot anlegen
-        #plt.bar(x, total_production,color='orange')
+        #plt.plot(x, net_value,'-',color='darkgreen')
-        #plt.bar(x, net_value,color='forestgreen')
+        plt.plot(x, net_value_mean,'-',color='darkgreen')
 
-        # Rollendes Mittel plotten
+        # Achsenbeschriftungen anlegen
-        #plt.plot(x, net_value_mean,'-',color='forestgreen')
-
-        # Titel und Achsenbeschriftungen anlegen
         plt.xlabel('Kalendertag')
         plt.ylabel('Leistung (W)')
-        #plt.title('Nettoleistung Nachbarschaft Standardfalll')
+
-        plt.title('Rollendes Mittel (7 Tage) Netto-Leistung Nachbarschaft Standardfall')
+        # Titel anlegen
+        #plt.title('Netto-leistung Nachbarschaft - Fall XXX')
+        plt.title('Erzeugung, Verbrauch und Netto-Leistung (Rollendes Mittel 24h) - Fall XXX')
 
         # X-Ticks nur monatlich plotten
         monthly_ticks = pd.date_range(start='2018-12-31', end ='2019-12-31', freq='1MS')
         plt.xticks(monthly_ticks)
 
         # Legende anlegen
-        #plt.legend(['Verbrauch', 'Erzeugung'], loc='upper right')
+        plt.legend(['Netto-Leistung', 'Leistung Verbrauch', 'Leistung Erzeugung'], loc='upper right')
-        #plt.legend(['Verbrauch', 'Erzeugung', 'Netto-Wert'], loc='upper right')
 
         # X-Labels rotieren
         plt.xticks(rotation=45)
 
         # Grid anlegen
+        plt.axhline(linewidth=1, color='black')
         plt.grid(color='black', axis='y', linestyle='--', linewidth=0.5)
         plt.show()