Bayesian bootstrap quantile regression for probabilistic photovoltaic power forecasting

Protection and Control of Modern Power Systems

Guido Carpinelli¹  Antonio Bracale²  Pierluigi Caramia²  Pasquale De Falco²  Mokhtar Bozorg³  Mauro Carpita³ 

[1] Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy;Department of Engineering, University of Naples Parthenope, Naples, Italy;Institute of Energy and Electrical Systems, School of Management and Engineering Vaud, University of applied sciences Western Switzerland (HES-SO), Yverdon-les-Bains, Switzerland;

关键词: Bayesian bootstrap;    Photovoltaic systems;    Probabilistic forecasting;    Renewable generation;    smart grids;   

DOI  :  10.1186/s41601-020-00167-7

来源: Springer

【 摘 要 】

Photovoltaic (PV) systems are widely spread across MV and LV distribution systems and the penetration of PV generation is solidly growing. Because of the uncertain nature of the solar energy resource, PV power forecasting models are crucial in any energy management system for smart distribution networks. Although point forecasts can suit many scopes, probabilistic forecasts add further flexibility to an energy management system and are recommended to enable a wider range of decision making and optimization strategies. This paper proposes methodology towards probabilistic PV power forecasting based on a Bayesian bootstrap quantile regression model, in which a Bayesian bootstrap is applied to estimate the parameters of a quantile regression model. A novel procedure is presented to optimize the extraction of the predictive quantiles from the bootstrapped estimation of the related coefficients, raising the predictive ability of the final forecasts. Numerical experiments based on actual data quantify an enhancement of the performance of up to 2.2% when compared to relevant benchmarks.

【 授权许可】

CC BY