Towards an efficient regression model for solar energy prediction

This paper describes a model for forecasting the daily solar energy. The features used in this model include precipitation, flux (long-wave, short wave), air pressure, humidity, cloud cover, temperature, radiation (long-wave and shortwave). These features along with previous data for daily solar energy received for the years 1994-2007 has been used for forecasting. The data for the features comes from a grid of sites in the United States and the data for previous years´ daily solar energy comes from 98 sites in Oklahoma, United States. Two algorithms have been used for forecasting - Linear Least Square Regression and Gradient Boosting Regression. Gradient Boosting Regression has shown to be around 2.5% more accurate as compared to Linear Least Square Regression.