Assessing stacked physics-informed machine learning models for co-located wind–solar power forecasting
Daniel Vázquez Pombo, Mario Javier Rincón, Peder Bacher, Henrik W. Bindner, Sergiu Spataru, Poul Ejnar Sørensen
Abstract
Increasingly advanced stochastic energy management systems are employed to facilitate the integration of wind and solar PV in worldwide power grids. In this context, forecasting is a key tool limiting the success of said control actions. This paper explores the suitability of stacked machine learning based models to predict wind and solar power available in the same site using a physics informed approach. The method recombines basic meteorological metrics widely available to compute new physics informed ones facilitating the learning procedure, while other are weak ML-models themselves. Further, to facilitate the integration of the point forecasters in the stochastic optimization field, we propose a simple unsupervised estimation of the error distribution. In this way, scenarios can be easily and homogeneously characterized for different resolutions and horizons. A study case is presented employing the Open Access dataset SOLETE, to facilitate benchmarking and replication of results. The results show accuracy improvements over the previously reported work over the same dataset.