Robust Assessment of Short-Term Wind Power Forecasting Models on Multiple Time Horizons
Fabrizio De Caro,
Jacopo De Stefani,
Alfredo A. Vaccaro and
Domenico D. Villacci
ULB Institutional Repository from ULB -- Universite Libre de Bruxelles
The massive penetration of renewable power generation in modern power grids is an effective way to reduce the impact of energy production on global warming. Unfortunately, the wind power generation may affect the regular operation of electrical systems, due to the stochastic and intermittent nature of the wind. For this reason, reducing the uncertainty about the wind evolution, e.g. by using short-term wind power forecasting methodologies, is a priority for system operators and wind producers to implement low-carbon power grids. Unfortunately, though the complexity of this task implies the comparison of several alternative forecasting methodologies and dimensionality reduction techniques, a general and robust procedure of model assessment still lacks in literature. In this paper the authors propose a robust methodology, based on extensive statistical analysis and resampling routines, to supply the most effective wind power forecasting method by testing a vast ensemble of methodologies over multiple time-scales and on a real case study. Experimental results on real data collected in an Italian wind farm show the potential of ensemble approaches integrating both statistical and machine learning methods.
Keywords: Wind Power Forecasting; Wind Energy; Robust Forecasting; Ensemble Forecasting (search for similar items in EconPapers)
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Published in: Technology and Economics of Smart Grids and Sustainable Energy (2020) v.5 n° 1
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