bifacial_radiance.mismatch.analysisIrradianceandPowerMismatch(testfolder, writefiletitle, portraitorlandscape, bififactor, numcells=72, downsamplingmethod='byCenter')[source]#

Use this when sensorsy calculated with bifacial_radiance > cellsy

Reads and calculates power output and mismatch for each file in the testfolder where all the bifacial_radiance irradiance results .csv are saved. First load each file, cleans it and resamples it to the numsensors set in this function, and then calculates irradiance mismatch and PVMismatch power output for averaged, minimum, or detailed irradiances on each cell for the cases of A) only 12 or 8 downsmaples values are considered (at the center of each cell), and B) 12 or 8 values are obtained from averaging all the irradiances falling in the area of the cell (No edges or inter-cell spacing are considered at this moment). Then it saves all the A and B irradiances, as well as the cleaned/resampled front and rear irradiances.

Ideally sensorsy in the read data is >> 12 to give results for the irradiance mismatch in the cell.

Also ideally n

  • testfolder (folder containing output .csv files for bifacial_radiance) –

  • writefiletitle (.csv title where the output results will be saved.) –

  • portraitorlandscape ('portrait' or 'landscape', for PVMismatch input) – which defines the electrical interconnects inside the module.

  • bififactor (bifaciality factor of the module. Max 1.0. ALL Rear irradiance values saved include the bifi-factor.) –

  • method (downsampling) –

  • Example

  • information. (# User) –

  • bifacial_radiance (import) –

  • testfolder=r'C (UserssayalaDocumentsHPC_ScratchEUPVSECHPC Tracking ResultsRICHMONDBifacial_Radiance ResultsPVPMC_0results') –

  • r'C (writefiletitle=) –

  • sensorsy=100

  • 'portrait' (portraitorlandscape =) –

  • analysis.analysisIrradianceandPowerMismatch(testfolder

  • writefiletitle

  • portraitorlandscape

  • bififactor=1.0

  • numcells=72)