Bayesian bootstrap quantile regression for probabilistic photovoltaic power forecasting

Bozorg, Mokhtar (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Bracale, Antonio (Department of Engineering, University of Naples Parthenope, Naples, Italy) ; Caramia, Pierluigi (Department of Engineering, University of Naples Parthenope, Naples, Italy) ; Carpinelli, Guido (Department of Electrical Engineering and Information Technology, University of Naples Frederico II, Naples, Italy) ; Carpita, Mauro (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; De Falco, Pasquale (Department of Engineering, University of Naples Parthenope, Naples, Italy)

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.


Article Type:
scientifique
Faculty:
Ingénierie et Architecture
School:
HEIG-VD
Institute:
IESE - Institut d'Energie et Systèmes Electriques
Date:
2020-09
Pagination:
12 p.
Published in:
Protection and Control of Modern Power Systems
Numeration (vol. no.):
2020, vol. 5, article no. 21
DOI:
ISSN:
2367-2617
Appears in Collection:



 Record created 2020-11-17, last modified 2020-11-17


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