Bayesian bootstrapping in real-time probabilistic photovoltaic power forecasting. (1st September 2021)

• Record Type: Journal Article
• Authors: Bozorg, Mokhtar; Bracale, Antonio; Carpita, Mauro; De Falco, Pasquale; Mottola, Fabio; Proto, Daniela
• Is Part Of: Solar energy. Volume 225(2021)
• Page Start: 577
• Page End: 590
• Publication Date: 2021-09-01
• Subjects: Bayesian bootstrap -- Photovoltaic power forecasting -- Probabilistic forecasting -- Renewable energy; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2021.07.063
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: The paper explores Bayesian bootstrap in probabilistic photovoltaic power forecasting. Bayesian bootstrap is applied to three probabilistic forecasting models. The optimal quantile is extracted from the sample bootstrap distribution. Bayesian bootstrap is compared with traditional bootstrap and with other benchmarks. Numerical experiments validate the Bayesian bootstrap performance on real data. Abstract: Modern distribution systems are characterized by increasing penetration of photovoltaic generation systems. Due to the uncertain nature of the solar primary source, photovoltaic power forecasting models must be developed in any energy management system for smart distribution networks. Although point forecasts can suit many scopes, probabilistic forecasts add further flexibility to any energy management system, and they are recommended to enable a wider range of decision making and optimization strategies. Real-time probabilistic photovoltaic power forecasting is performed in this paper by using an approach based on Bayesian bootstrap. Particularly, the Bayesian bootstrap is applied to three probabilistic forecasting models (i.e., linear quantile regression, gradient boosting regression tree and quantile regression neural network) to provide sample bootstrap distributions of the predictive quantiles of photovoltaic power. The heterogeneous nature of the selected models allows evaluating the performance of the Bayesian bootstrap within different forecasting frameworks. Several benchmarks and error indices and scores are used to assess the performance of Bayesian bootstrap in probabilistic photovoltaic power forecasting. Tests carried out on two actual photovoltaic power datasets for probabilistic forecasting demonstrates the effectiveness of the proposed approach.