Ensemble Methods for Solar Power Forecasting

We consider the task of predicting the solar power generated by a photovoltaic system, one-step ahead, from previous half-hourly photovoltaic power data. We propose a range of strategies for constructing static and dynamic heterogeneous ensembles and conduct an extensive evaluation using data for two years from two Australian solar power plants. We analyse the performance of the proposed static and dynamic ensembles and compare them with classical ensemble methods (bagging, boosting and random forest) and a baseline, showing an improved performance. The best result was achieved by the dynamic ensembles DEPast and DEPast+Future, in conjunction with the Peer Check algorithm for selecting base learners for inclusion in the dynamic ensembles.