adding Bififactor to analysis
multiplying all Grear by the bififactor from the get-go.
@@ -15,7 +15,7 @@ from statsmodels import robust
import pandas as pd
-def analysisIrradianceandPowerMismatch(testfolder, writefiletitle, sensorsy, portraitorlandscape):
+def analysisIrradianceandPowerMismatch(testfolder, writefiletitle, sensorsy, portraitorlandscape, bififactor):
r'''
Use this when sensorsy calculated with bifacial_radiance > cellsy
@@ -37,13 +37,13 @@ def analysisIrradianceandPowerMismatch(testfolder, writefiletitle, sensorsy, por
----------
testfolder: folder containing output .csv files for bifacial_radiance
writefiletitle: .csv title where the output results will be saved.
- numpanels:
portraitorlandscape: 'portrait' or 'landscape', for PVMismatch input
which defines the electrical interconnects inside the module.
sensorsy : number of sensors. Ideally this number is >> 12 and
is also similar to the number of sensors (points) in the .csv result files.
We want more than 12 sensors to be able to calculate mismatch of
irradiance in the cell.
+ bififactor: bifaciality factor of the module. Max 1.0. ALL Rear irradiance values saved include the bifi-factor.
Exampple:
# User information.
@@ -75,6 +75,7 @@ def analysisIrradianceandPowerMismatch(testfolder, writefiletitle, sensorsy, por
F[filelist[z]]=frontres
B[filelist[z]]=backres
+ B = B*bififactor
# Downsample routines:
if sensorsy > cellsy:
F_cellcenter = sensorsdownsampletocellbyCenter(F, cellsy)
