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pv_input auskommentiert

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Patrick Hangl
2024-12-16 10:39:59 +01:00
parent 047ee6ee0f
commit 1cf5b45563
1 changed files with 21 additions and 7 deletions
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24
pv_input.py
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@@ -1,3 +1,13 @@
# 3 Möglichkeiten für Ertragsberechnung: Clear Sky Modell, TMY oder POA-Data
# Clear Sky: Strahlungsmodell, welches theoretische Strahlungsdaten an bestimmten Standpunkt enthält welche eine flache Oberfläche treffen - vewerndet g(i)-Strahlungsdaten
# TMY = Typical Meteorolical Year - reale Strahlungsdaten welche eine flache Oberfläche treffen - vewerndet g(i)-Strahlungsdaten
# POA = Plane of Array - was das PV-Modul (und dessen Ausrichtung) wirklich trifft - verwendet POA-Strahlungsdaten
# G(i), poa_global = Global irradiance on inclined plane (W/m^2)
# Gb(i), poa_direct = Beam (direct) irradiance on inclined plane (W/m^2)
# Gd(i), poa_sky_diffuse = Diffuse irradiance on inclined plane (W/m^2)
# Gr(i), poa_ground_diffuse = Reflected irradiance on inclined plane (W/m^2)
import pvlib import pvlib
import pandas as pd import pandas as pd
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
@@ -39,7 +49,8 @@ modelchain = ModelChain(system, location)
# Ertragssimulation mit Clear-Sky Modell # Ertragssimulation mit Clear-Sky Modell
# times = pd.date_range(start='2021-07-01', end ='2021-07-07', freq='1h', tz=location.tz) # Index-Spalte mit Zeiten für clear-sky Dataset anlegen
# times = pd.date_range(start='2020-01-01', end ='2020-12-31', freq='1h', tz=location.tz)
# clear_sky = location.get_clearsky(times) # clear_sky = location.get_clearsky(times)
# #clear_sky.plot(figsize=(16,9)) # #clear_sky.plot(figsize=(16,9))
@@ -48,21 +59,24 @@ modelchain = ModelChain(system, location)
# Ertragssimulation mit realen Strahlungsdaten aus Wetterjahr # Ertragssimulation mit realen Strahlungsdaten aus Wetterjahr
# Hier ist Süden Azimuth = 0 # Hier ist Süden Azimuth = 0°, bei PVLib ist es 180°
# POA = Plane Of Array
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, 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-180, outputformat='json', usehorizon=True, userhorizon=None, pvcalculation=False, peakpower=None,
pvtechchoice='crystSi', mountingplace='free', loss=0, trackingtype=0, optimal_surface_tilt=False, optimalangles=False, 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) url='https://re.jrc.ec.europa.eu/api/', map_variables=True, timeout=30)
# Spaltennamen umschreiben, sodass Modelchain sie verwenden kann
# Notwendige Spalten ausrechnen und hinzufügen, sodass Modelchain sie verwenden kann
poa_data['poa_diffuse'] = poa_data['poa_sky_diffuse'] + poa_data['poa_ground_diffuse'] poa_data['poa_diffuse'] = poa_data['poa_sky_diffuse'] + poa_data['poa_ground_diffuse']
poa_data['poa_global'] = poa_data['poa_diffuse'] + poa_data['poa_direct'] poa_data['poa_global'] = poa_data['poa_diffuse'] + poa_data['poa_direct']
# Daten in csv exportieren
#poa_data.to_csv('poa_data.csv') #poa_data.to_csv('poa_data.csv')
# Index des Dataframe mit datetime-Index von Pandas überschreiben # Index des Dataframe mit datetime-Index von Pandas überschreiben
poa_data.index = pd.to_datetime((poa_data.index)) poa_data.index = pd.to_datetime((poa_data.index))
# Diese Funktion benötigt POA-Daten anstatt g(i)-Daten -> DOKU
modelchain.run_model_from_poa(poa_data) modelchain.run_model_from_poa(poa_data)
# Ergebnis plotten
modelchain.results.ac.plot(figsize=(16,9)) modelchain.results.ac.plot(figsize=(16,9))
plt.show() plt.show()