问为什么北半球pvlib的最佳surface_azimuth不在180°左右？

EN

import os
import pandas as pd
import numpy as np
import os
import os.path
import matplotlib.pyplot as plt
import pvlib
from geopy.exc import GeocoderTimedOut 
from geopy.geocoders import Nominatim 
from IPython.display import Image


## GET THE LATITUDE & LONGITUDE OF A GIVEN CITY
geolocator = Nominatim(user_agent="____'s app") 
geo = geolocator.geocode("Berkeley") 
## CHECK THAT CITY IS CORRECT (by Country, State, etc.)
print(geo.address)
# CHECK THE LAT, LON order
print(geo.latitude, geo.longitude)


## SELECT THE YEAR & TIMES YOU'D LIKE TO MODEL OFF
YEAR = 2019
STARTDATE = '%d-01-01T00:00:00' % YEAR
ENDDATE = '%d-12-31T23:59:59' % YEAR
TIMES = pd.date_range(start=STARTDATE, end=ENDDATE, freq='H')

## ACCESS THE NREL API TO EXTRACT WEATHER DATA
NREL_API_KEY = os.getenv('NREL_API_KEY', 'DEMO_KEY')
## FILL IN THE BLANK WITH YOUR EMAIL ADRRESS
EMAIL = os.getenv('EMAIL', '_______.com')

##NEED TO COMMENT OUT THIS LINE BELOW -- if you call it too many times within an hour, it will break your code
header, data = pvlib.iotools.get_psm3(LATITUDE, LONGITUDE, NREL_API_KEY, EMAIL)


## SELECT THE PVLIB PANEL & INTERVTER YOU'D LIKE TO USE
## CAN ALSO CHOOSE FROM SANDIA LABS' DATASET OF PANELS & INVERTERS (check out the function)
## WE CHOSE THE CECMods because they highlighted the ones that were BIPV
INVERTERS = pvlib.pvsystem.retrieve_sam('CECInverter')
INVERTER_10K = INVERTERS['SMA_America__SB10000TL_US__240V_']
CECMODS = pvlib.pvsystem.retrieve_sam('CECMod')

## SELECT THE PANEL YOU'D LIKE TO USE (NOTE: THE PEVAFERSA MODEL IS A BIPV PANEL)
CECMOD_MONO = CECMODS['Pevafersa_America_IP_235_GG']


## CREATING AN ARRAY TO ITERATE THROUGH IN ORDER TO TEST DIFFERENT SURFACE_AZIMUTHS
heading_array = np.arange(0, 361, 2)
heading_array

heading_DF = pd.DataFrame(heading_array).rename(columns = {0: "Heading"})
heading_DF.head()


# geo IS AN OBJECT (the given city) CREATED ABOVE
LATITUDE, LONGITUDE = geo.latitude, geo.longitude

# data IS AN OBJECT (the weather patterns) CREATED ABOVE
# TIMES IS ALSO CREATED ABOVE, AND REPRESENTS TIME
data.index = TIMES
dni = data.DNI.values
ghi = data.GHI.values
dhi = data.DHI.values
surface_albedo = data['Surface Albedo'].values
temp_air = data.Temperature.values
dni_extra = pvlib.irradiance.get_extra_radiation(TIMES).values

# GET SOLAR POSITION
sp = pvlib.solarposition.get_solarposition(TIMES, LATITUDE, LONGITUDE)
solar_zenith = sp.apparent_zenith.values
solar_azimuth = sp.azimuth.values


# CREATING THE ARRY TO STORE THE DAILY ENERGY OUTPUT BY SOLAR AZIMUTH
e_by_az = []


# IDEAL surface_tilt ANGLE IN NORTHERN HEMISPHERE IS ~25
surface_tilt = 25

# ITERATING THROUGH DIFFERENT SURFACE_AZIMUTH VALUES
for heading in heading_DF["Heading"]:
    
 
    surface_azimuth = heading

    poa_sky_diffuse = pvlib.irradiance.get_sky_diffuse(
        surface_tilt, surface_azimuth, solar_zenith, solar_azimuth,
        dni, ghi, dhi, dni_extra=dni_extra, model='haydavies')

    # calculate the angle of incidence using the surface_azimuth and (hardcoded) surface_tilt
    aoi = pvlib.irradiance.aoi(
        surface_tilt, surface_azimuth, solar_zenith, solar_azimuth)
    # https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.irradiance.aoi.html
    # https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.pvsystem.PVSystem.html

    poa_ground_diffuse = pvlib.irradiance.get_ground_diffuse(
        surface_tilt, ghi, albedo=surface_albedo)
    poa = pvlib.irradiance.poa_components(
        aoi, dni, poa_sky_diffuse, poa_ground_diffuse)
    poa_direct = poa['poa_direct']
    poa_diffuse = poa['poa_diffuse']
    poa_global = poa['poa_global']
    iam = pvlib.iam.ashrae(aoi)
    effective_irradiance = poa_direct*iam + poa_diffuse
    temp_cell = pvlib.temperature.pvsyst_cell(poa_global, temp_air)

    # THIS IS THE MAGIC
    cecparams = pvlib.pvsystem.calcparams_cec(
        effective_irradiance, temp_cell,
        CECMOD_MONO.alpha_sc, CECMOD_MONO.a_ref,
        CECMOD_MONO.I_L_ref, CECMOD_MONO.I_o_ref,
        CECMOD_MONO.R_sh_ref, CECMOD_MONO.R_s, CECMOD_MONO.Adjust)
    # mpp is the list of energy output by hour for the whole year using a single panel
    mpp = pvlib.pvsystem.max_power_point(*cecparams, method='newton')
    mpp = pd.DataFrame(mpp, index=TIMES)
    first48 = mpp[:48]
    Edaily = mpp.p_mp.resample('D').sum()
    # Edaily is the list of energy output by day for the whole year using a single panel

    Eyearly = sum(Edaily)
    
    e_by_az.append(Eyearly)


## LINKING THE Heading (ie. surface_azimuth) AND THE Eyearly (ie. yearly energy output) IN A DF
heading_DF["Eyearly"] = e_by_az
heading_DF.head()


## VISUALIZE ENERGY OUTPUT BY SURFACE_AZIMUTH
plt.plot(heading_DF["Heading"], heading_DF["Eyearly"])
plt.xlabel("Surface_Azimuth Angle")
plt.ylabel("Yearly Energy Output with tilt @ " + str(surface_tilt))
plt.title("Yearly Energy Output by Solar_Azimuth Angle using surface_tilt = " + str(surface_tilt) + " in Berkeley, CA");

# FIND SURFACE_AZIMUTH THAT YIELDS THE MAX ENERGY OUTPUT
heading_DF[heading_DF["Eyearly"] == max(heading_DF["Eyearly"])]

# FIND SURFACE_AZIMUTH THAT YIELDS THE MIN ENERGY OUTPUT
heading_DF[heading_DF["Eyearly"] == min(heading_DF["Eyearly"])]