Machine learning predicts residential power yield of large PV system fleets – pv magazine International

Scientists at the Delft University of Technology in the Netherlands have developed a machine-learning (ML) technique to predict power yields in rooftop PV system. They claim it can predict electricity generation levels one hour ahead. They described their findings in "Individual yield nowcasting for residential PV systems," which was recently published in Solar Energy. The researchers said the new approach can forecast the individual power output of large fleets of PV systems. Their novel method is based on a single XGBoost algorithm, which is a decision-tree ensemble, open-access algorithm that uses a gradient-boosting framework.