Assessing stacked physics-informed machine learning models for co-located wind–solar power forecasting

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 characterised 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. • Proposes a methodology to expand basic meteorological metrics into physics informed features. • The simplicity of the method allows its implementation in virtually any wind or solar installation. • An unsupervised error characterisation for point forecasters allows their implementation in stochastic optimization. • The accuracy surpasses previously reported work on the same open access dataset, SOLETE.