Influential Variable Selection for Improving Solar Forecasts from Numerical Weather Prediction

This paper presents two statistical methods for refining predicted solar irradiance from Numerical Weather Prediction (NWP). Firstly, the spatial averaging is used to reduce the variance of solar irradiance prediction over a spatial area of interest. Secondly, a model output statistics method which is a linear regression model, is applied to explain relationships between solar irradiance and relevant weather variables. In this study, we perform a variable selection based on three statistical methods: partial correlation, stepwise regression, and subset regression, to specify the important meteorological variables for solar irradiance prediction in Thailand. The results show that spatial averaging of NWP forecasts over $7 \times 7$ grid points provides a reduction of 3.42% Normalized Root Mean Square Error (NRMSE) from NWP forecasts. Model output statistics with and without variable selection can further improve output from spatial averaging by 41.32% and 41.89% NRMSE respectively. We conclude that highly relevant variables including irradiance measurements, ambient temperature, relative humidity, UV index, and NWP forecasts are sufficient for a significant improvement of NWP forecasts in Thailand.