Hourly Day Ahead Solar Irradiance Forecasting Model in LabVIEW Using Cloud Cover Data

Oğuzhan Ceylan, Michael Starke, Phil Irminger, Ben Ollis, Dan King, Kevin Tomsovic
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Abstract


This paper applies a regression based numerical method for hourly forecasting photovoltaic power output. This methodology uses a historical dataset composed of irradiance, azimuth, zenith angle and time of day information. A developed forecast program from this methodology pulls publicly available cloud cover forecast data for the following day and uses a numerical regression based method for fitting the data. Using publicly available temperature forecast data, forecasted irradiance data, and computed solar position (zenith, azimuth) data, both power output and temperature module output of PV array is computed. Numerical forecast results are compared to actual collected data. 

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