我有以下脚本：
import itertools
import pandas as pd
import pvlib
from pvlib import clearsky, atmosphere
from pvlib.location import Location
cs = pvlib.location.get_clearsky_test(50.430104, -5.006048, 'ineichen',
1525953600000000000, 'test')
print("ghi = " + str(cs['ghi'][0]))
print("dni = " + str(cs['dni'][0]))
print("dhi = " + str(cs['dhi'][0]))
# Parameters: Latitude, Longitude, Tilt, Azimuth, ghi, dni, dhi, time (Epoch)
result = pvlib.irradiance.globalinplane_test_output(50.430104, -5.006048,
24, 180, cs['ghi'][0], cs['dni'][0], cs['dhi'][0], 1525953600000000000, .25,
'grass')
print (result)
我这里有一些包装方法，但是将调用底层方法。这将产生以下结果：
ineichen
ghi = 861.5440345805347
dni = 893.789882747542
dhi = 111.15085948276032
Solar Position azimuth = 172.79194271695087
Solar Position apparent_zenith = 32.90596711532058
AOI = 9.530542460995187
Airmass = 1.19028427148373
Gr-grass = 7.448439523040739
Time = 2018-05-10 12:00:00
Diff Perez = 125.92483957453679
Global in plane = poa_global poa_direct poa_diffuse
2018-05-10 12:00:00 1014.826609 881.45333 133.373279
poa_global poa_direct poa_diffuse
2018-05-10 12:00:00 1014.826609 881.45333 133.373279
5月10日的辐照度比在该位置用比热计测量的辐照度低约10％。据我了解，该模型通常低于实测数据。
我有什么办法可以尝试改善正在生成的“理论”数字，因为这些数字似乎低于真实数字？