A recursive ensemble model for forecasting the power output of photovoltaic systems

Solar power provides a clean and renewable energy source. However, unlike many conventional sources, Photovoltaic (PV) power generation is of high volatility and uncertainty in short terms, which creates great challenges to forecasting and balancing electricity generation with demand. This study investigates the effects of PV solar power variability and proposes a data-driven ensemble modeling technique to improve the prediction accuracy of PV power generation. Three different types of models are integrated within a recursive arithmetic average model on their stand-alone predictions. The proposed methodology is later demonstrated to be of higher accuracy by comparing its prediction performance with each stand-alone forecasting model. Several different training and testing samples have been analyzed with the proposed model. The results show that the ensemble model performs better than the other stand-alone forecasting techniques in general.