Machine learning models were developed in order to forecast weather variables such assolar radiation, temperature, and wind speed for one to 24 hours in advance. Weather predictions and ground truth weather observations were sourced from the National Oceanic and AtmosphericAdministration (NOAA) and the Georgia Automated Environmental Monitoring Network(GAEMN) for five cities in Georgia. Results indicate that incorporating weather forecastsbecomes increasingly more important for accurate solar radiation prediction at longer predictionwindows, and also that postprocessing of NOAA's weather forecasts can drastically improveaccuracy beyond usage of the raw forecasts alone.
