Fine-Grained Photovoltaic Output Prediction Using a Bayesian Ensemble

Abstract

Local and distributed power generation is increasingly reliant on renewable power sources, e.g., solar (photovoltaic or PV) and wind energy. The integration of such sources into the power grid is challenging, however, due to their variable and intermittent energy output. To effectively use them on a large scale, it is essential to be able to predict power generation at a fine-grained level. We describe a novel Bayesian ensemble methodology involving three diverse predictors. Each predictor estimates mixing coefficients for integrating PV generation output profiles but captures fundamentally different characteristics. Two of them employ classical parameterized (naive Bayes) and non-parametric (nearest neighbor) methods to model the relationship between weather forecasts and PV output. The third predictor captures the sequentiality implicit in PV generation and uses motifs mined from historical data to estimate the most likely mixture weights using a stream prediction methodology. We demonstrate the success and superiority of our methods on real PV data from two locations that exhibit diverse weather conditions. Predictions from our model can be harnessed to optimize scheduling of delay tolerant workloads, e.g., in a data center.