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

Title: A copula-based Bayesian method for probabilistic solar power forecasting

Abstract

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.

Authors:

Panamtash, Hossein ^[1];

^[1];

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

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

Publication Date:: Fri Dec 20 00:00:00 EST 2019

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

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

OSTI Identifier:: 1820624

Grant/Contract Number:: EE0007998

Resource Type:: Accepted Manuscript

Journal Name:: Solar Energy

Additional Journal Information:: Journal Volume: 196; Journal ID: ISSN 0038-092X

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 24 POWER TRANSMISSION AND DISTRIBUTION; Bayesian inference; Solar power forecasting; Copulas; Probabilistic forecasting

Citation Formats


                    Panamtash, Hossein, Zhou, Qun, Hong, Tao, Qu, Zhihua, and Davis, Kristopher O. A copula-based Bayesian method for probabilistic solar power forecasting.  United States: N. p., 2019. 
Web.  doi:10.1016/j.solener.2019.11.079.

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                    Panamtash, Hossein, Zhou, Qun, Hong, Tao, Qu, Zhihua, & Davis, Kristopher O. A copula-based Bayesian method for probabilistic solar power forecasting.  United States.  https://doi.org/10.1016/j.solener.2019.11.079

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                    Panamtash, Hossein, Zhou, Qun, Hong, Tao, Qu, Zhihua, and Davis, Kristopher O. Fri .  
"A copula-based Bayesian method for probabilistic solar power forecasting".  United States.  https://doi.org/10.1016/j.solener.2019.11.079.  https://www.osti.gov/servlets/purl/1820624.

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@article{osti_1820624,

  title        = {A copula-based Bayesian method for probabilistic solar power forecasting},

  author       = {Panamtash, Hossein and Zhou, Qun and Hong, Tao and Qu, Zhihua and Davis, Kristopher O.},

  abstractNote = {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.},

  doi          = {10.1016/j.solener.2019.11.079},

  journal      = {Solar Energy},

  number       = ,

  volume       = 196,

  place        = {United States},

  year         = {Fri Dec 20 00:00:00 EST 2019},

  month        = {Fri Dec 20 00:00:00 EST 2019}

}

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Title: A copula-based Bayesian method for probabilistic solar power forecasting

Abstract

Citation Formats

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