diff --git a/neighborhood.py b/neighborhood.py
index 8ce8968..ad33ee2 100644
--- a/neighborhood.py
+++ b/neighborhood.py
@@ -63,30 +63,45 @@ class Neighborhood:
     # Gesamterzeugung, Gesamtverbrauch und Nettoverbrauch plotten
     def plot_consumption(self):
         total_consumption = -1*(self.consumer.final_consumption + self.producer.final_consumption)
+        total_consumption_mean = total_consumption.rolling(168).mean()
         total_production = self.producer.final_production
+        total_production_mean = total_production.rolling(168).mean()
         net_value = total_consumption + total_production
-
+        net_value_mean = net_value.rolling(168).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')
         x = x[:-1]
+
         # Plot dimensionieren
-        plt.figure(figsize=(10, 6))
-        # y-Werte den x-Werten zuordnen
-        plt.bar(x, total_consumption)
-        plt.bar(x, total_production)
-        plt.bar(x, net_value)
-        #plt.plot(x, net_value, '-')
+        plt.figure(figsize=(15, 9))
+
+        # Barplot anlegen
+
+        #plt.bar(x, total_consumption)
+        #plt.bar(x, total_production,color='orange')
+        #plt.bar(x, net_value,color='forestgreen')
+        
+        # Rollendes Mittel plotten
+        #plt.plot(x, net_value_mean,'-',color='forestgreen')
+
         # Titel und Achsenbeschriftungen anlegen
-        plt.xlabel('Stunde')
-        plt.ylabel('Strom (kWh)')
-        plt.title('Produktion/Verbrauch Nachbarschaft')
+        plt.xlabel('Kalendertag')
+        plt.ylabel('Leistung (W)')
+        #plt.title('Nettoleistung Nachbarschaft Standardfalll')
+        plt.title('Rollendes Mittel (7 Tage) Netto-Leistung Nachbarschaft Standardfall')
+
         # 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', 'Produktion', 'Netto-Wert'], loc='upper right')
+        #plt.legend(['Verbrauch', 'Erzeugung'], loc='upper right')
+        #plt.legend(['Verbrauch', 'Erzeugung', 'Netto-Wert'], loc='upper right')
+
         # X-Labels rotieren
         plt.xticks(rotation=45)
+
         # Grid anlegen
         plt.grid(color='black', axis='y', linestyle='--', linewidth=0.5)
         plt.show()
diff --git a/pv_input.py b/pv_input.py
index 2c0d795..30fbe46 100644
--- a/pv_input.py
+++ b/pv_input.py
@@ -20,8 +20,6 @@ from pvlib.temperature import TEMPERATURE_MODEL_PARAMETERS
 latitude = 47.2675
 longitude = 11.3910
 tz = 'Europe/Vienna'
-surface_tilt = 0
-surface_azimuth = 180
 year = 2019
 
 # Daten für Datenbank -> Module + WR
@@ -33,8 +31,8 @@ inverter = database_inverter['ABB__PVI_4_2_OUTD_US__208V_']
 # PV-Anlage definieren
 modules_per_string = 10
 strings_per_inverter = 2
-surface_tilt = 0
-surface_azimuth = 180
+surface_tilt = 20
+surface_azimuth = 150
 
 # Temperaturparameter definieren
 temperature_parameters = TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_glass']
@@ -62,7 +60,7 @@ def create_production_profile():
 
     # 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, 
-                                surface_azimuth=surface_azimuth-180, outputformat='json', usehorizon=True, userhorizon=None, pvcalculation=False, peakpower=None, 
+                                surface_azimuth=surface_azimuth-150, outputformat='json', usehorizon=True, userhorizon=None, pvcalculation=False, peakpower=None, 
                                 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)