112 lines
4.3 KiB
Python
112 lines
4.3 KiB
Python
# 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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self.quantity = quantity
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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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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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# Verbrauchsprofil mit der Anzahl der 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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# Erzegungsprofil über PV_Input.py erstelln, in .csv schreiben und von dort dann die Leistungs-Series extrahieren
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def create_production_profile(self):
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production_profile = pv.create_production_profile()
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production_profile.to_csv('production_profile.csv', sep=';',header=['Leistung'])
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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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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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class Consumer:
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def __init__(self, quantity):
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self.quantity = quantity
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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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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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# Verbrauchsprofil mit der Anzahl der 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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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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# 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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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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# 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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x = x[:-1]
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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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#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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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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# 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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# X-Labels rotieren
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plt.xticks(rotation=45)
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# Grid anlegen
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plt.grid(color='black', axis='y', linestyle='--', linewidth=0.5)
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plt.show()
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