A copula-based Bayesian method for probabilistic solar power forecasting

Panamtash, Hossein; Zhou, Qun; Hong, Tao; Qu, Zhihua; Davis, Kristopher O.

doi:10.1016/j.solener.2019.11.079

A copula-based Bayesian method for probabilistic solar power forecasting

Journal Article · Fri Dec 20 00:00:00 EST 2019 · Solar Energy

DOI:https://doi.org/10.1016/j.solener.2019.11.079· OSTI ID:1820624

Panamtash, Hossein ^[1]; ^[2]; ^[3]; Qu, Zhihua ^[2]; Davis, Kristopher O. ^[2]

Univ. of Central Florida, Orlando, FL (United States); University of Central Florida
Univ. of Central Florida, Orlando, FL (United States)
Univ. of North Carolina, Charlotte, NC (United States)

With increased penetration of solar energy sources, solar power forecasting has become more crucial and challenging. This paper proposes a copula-based Bayesian approach to improve probabilistic solar power forecasting by capturing the joint distribution between solar power and ambient temperature. A prior forecast distribution is first obtained using different underlying point forecasting models. Parametric and empirical copulas of solar power and temperature are then developed to update the prior distribution to the posterior forecast distribution. A public solar power database is used to demonstrate effectiveness of the proposed method. Numerical results show that the copula-based Bayesian method outperforms the forecasting method that directly uses temperature as a feature. The Bayesian method is also compared with persistent models and show improved performance. Furthermore, this article includes supplementary material (data and code) for reproducibility.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of Central Florida, Orlando, FL (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

Grant/Contract Number:: EE0007998

OSTI ID:: 1820624

Journal Information:: Solar Energy, Journal Name: Solar Energy Vol. 196; ISSN 0038-092X

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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