A Probabilistic Competitive Ensemble Method for Short-Term Photovoltaic Power Forecasting

Photovoltaic systems are expected to play a key role in the planning and operation of future distribution systems due to the benefits associated with their use. Unfortunately, a great problem is involved in photovoltaic power utilization, i.e., the unpredictability of the solar source. Thus, many forecasting methods have been developed in order to provide tools with adequate consistency, quality, and value. The methods can provide either deterministic or probabilistic forecasts; the latter seem to be the most appropriate for taking into account the unavoidable uncertainties of the solar source. In this paper, a new probabilistic method based on a competitive ensemble of different base predictors is proposed for the short-term forecasting of photovoltaic power. Three probabilistic methods were selected and trained as base predictors in order to obtain an ensemble of the predictive distribution with optimal characteristics of sharpness and reliability. Numerical applications based on actual data were performed to test the effectiveness of the proposed method with respect to single predictors and to a benchmark method.