A Comparative Analysis of Tree-Based Models for Day-Ahead Solar Irradiance Forecasting

Abstract

Recently, solar photovoltaic (PV) techniques have been attracting lots of attention for sustainable development, and solar irradiance forecasting is crucial to estimate PV output. However, accurate solar irradiance forecasting is challenging because solar irradiance exhibits complex patterns due to various weather factors. Decision tree (DT)-based methods can effectively train complex internal and external factors so that they have been widely used in energy forecasting. In this paper, we developed several solar irradiation forecasting models using tree-based methods such as DT, bagging, random forest, gradient boosting machine, extreme gradient boosting, and Cubist. We then compared their prediction performance in terms of mean square error, root-mean-square-error (RMSE), and normalized RMSE. Experiment results for two regions on Jeju Island showed that Cubist could derive better prediction performance of day-ahead hourly solar irradiation than other tree-based methods.