Using Global POA Data for Simulation with pvlib #2188
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Hello, I have two datasets that I'd like to use as input for my simulation. The datasets are as follows:
model_chain = ModelChain(system, site_location, weather = pd.DataFrame([dat["dni"], dat["ghi"], dat["dhi"], dat["temp_air"], dat["wind_speed"]]).T
My question is: How can I perform the simulation using only the Global POA data? Any guidance or suggestions would be greatly appreciated. Thank you! |
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The easy option is to ignore aoi losses (then you don't need to know the split between poa_diffuse and poa_direct) If you want to include aoi losses then you need to decompose the poa_global irradiance into poa_direct and poa_diffuse. I'm aware of two algorithms in pvlib that are useful for this: gti_dirint and ghi_from_poa_diresse_2023. These two models will give you the ghi and ghi/dni/dhi respectively. You then have to transpose these components to your surface again but that will give you poa_diffuse and poa_direct. The sum of the derived poa_direct and poa_diffuse may not exactly match your original measured poa_global, which is why I personally would make some adjustments to the derived poa_diffuse/poa_direct to make sure they add up to poa_global. |
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The easy option is to ignore aoi losses (then you don't need to know the split between poa_diffuse and poa_direct)
If you want to include aoi losses then you need to decompose the poa_global irradiance into poa_direct and poa_diffuse. I'm aware of two algorithms in pvlib that are useful for this: gti_dirint and ghi_from_poa_diresse_2023. These two models will give you the ghi and ghi/dni/dhi respectively. You then have to transpose these components to your surface again but that will give you poa_diffuse and poa_direct. The sum of the derived poa_direct and poa_diffuse may not exactly match your original measured poa_global, which is why I personally would make some adjustments to the der…