PV-Ausrichtung angepasst, Neighborhood formatiert
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@@ -63,30 +63,45 @@ class Neighborhood:
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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_consumption_mean = total_consumption.rolling(168).mean()
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total_production = self.producer.final_production
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total_production_mean = total_production.rolling(168).mean()
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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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# 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=(10, 6))
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# y-Werte den x-Werten zuordnen
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plt.bar(x, total_consumption)
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plt.bar(x, total_production)
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plt.bar(x, net_value)
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#plt.plot(x, net_value, '-')
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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('Stunde')
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plt.ylabel('Strom (kWh)')
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plt.title('Produktion/Verbrauch Nachbarschaft')
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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', 'Produktion', 'Netto-Wert'], loc='upper right')
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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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