Multi-Model Ensemble for Day Ahead PV Power Forecasting Improvement
Bucci F Cornaro C
De Felice M
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At: EU PV SEC ; Munich ; 20.6.2016 - 24.6.2016 ; The aim of the paper is to compare several data-driven PV power forecast models using different Numerical Weather Prediction (NWP) input data and then to build up an outperforming Multi-Model Ensemble (MME) and its prediction intervals. Statistic, stochastic and hybrid machine-learning algorithms were developed and the NWP data from IFS and WRF models were used as input. The MME built up using data-driven forecast produced by the different models improves the performance of the best model of the ensemble, bringing the skill score from 42% to 46%.