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