Dynamic Ensemble Using Previous and Predicted Future Performance for Multi-step-ahead Solar Power Forecasting

Abstract

We consider the task of predicting the solar power generated by a photovoltaic system, for multiple steps ahead, from previous solar power data. We propose DEN-PF, a dynamic heterogeneous ensemble of prediction models, which weights the individual predictions by considering two components – the ensemble member’s error on recent data and its predicted error for the new time points. We compare the performance of DEN-PF with dynamic ensembles using only one of these components, a static ensemble, the single models comprising the ensemble and a baseline. The evaluation is conducted on data for two years, sampled every 5 min, for prediction horizons from 5 to 180 min ahead, under three prediction strategies: direct, iterative and direct-ds, which uses downsampling. The results show the effectiveness of DEN-PF and the benefit of considering both error components for the direct and direct-ds strategies. The most accurate prediction model was DEN-PF using the direct-ds strategy.