Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression

Abstract

The variability of renewable energy resources, due to the characteristic weather fluctuations, introduces uncertainty in generation output that are greater than the conventional energy reserves the grid uses to deal with the relatively predictable uncertainties in demand. The high variability of renewable generation makes forecasting critical for optimal balancing and dispatch of generation plants in a smarter grid. The challenge is to improve the accuracy and the confidence level of forecasts at a reasonable computational cost. Ensemble methods such as random forest (RF) and extra trees (ET) are well suited for predicting stochastic photovoltaic (PV) generation output as they reduce variance and bias by combining several machine learning techniques while improving the stability; i.e. generalisation capabilities. This paper investigated the accuracy, stability and computational cost of RF and ET for predicting hourly PV generation output, and compared their performance with support vector regression (SVR), a supervised machine learning technique. All developed models have comparable predictive power and are equally applicable for predicting hourly PV output. Despite their comparable predictive power, ET outperformed RF and SVR in terms of computational cost. The stability and algorithmic efficiency of ETs make them an ideal candidate for wider deployment in PV output forecasting.