A two-step short-term probabilistic wind forecasting methodology based on predictive distribution optimization

Sun, Mucun; Feng, Cong; Chartan, Erol Kevin; Hodge, Bri-Mathias S.; Zhang, Jie

doi:10.1016/j.apenergy.2019.01.182

Title: A two-step short-term probabilistic wind forecasting methodology based on predictive distribution optimization

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Abstract

With increasing wind penetrations into electric power systems, probabilistic wind forecasting becomes more critical to power system operations because of its capability of quantifying wind uncertainties. In this paper, a two-step probabilistic wind forecasting approach based on pinball loss optimization is developed. First, a multimodel machine learning-based ensemble deterministic forecasting framework is adopted to generate deterministic forecasts. The deterministic forecast is assumed to be the mean value of the predictive distribution at each forecasting time stamp. Then, the optimal unknown parameter (i.e., standard deviation) of the predictive distribution is estimated by a support vector regression surrogate model based on the deterministic forecasts. Lastly, probabilistic forecasts are generated from the predictive distribution. Numerical results of case studies at eight locations show that the developed two-step probabilistic forecasting methodology has improved the pinball loss metric score by up to 35% compared to a baseline quantile regression forecasting model.

Authors:

Sun, Mucun ^[1];

^[1]; Chartan, Erol Kevin ^[2]; Hodge, Bri-Mathias S. ^[3]; Zhang, Jie ^[1]

Univ. of Texas at Dallas, Richardson, TX (United States)
National Renewable Energy Lab. (NREL), Golden, CO (United States)
National Renewable Energy Lab. (NREL), Golden, CO (United States); Univ. of Colorado, Boulder, CO (United States)

Publication Date:: Thu Jan 31 00:00:00 EST 2019

Research Org.:: National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W); WindView; USDOE Grid Modernization Laboratory Consortium

OSTI Identifier:: 1494978

Alternate Identifier(s):: OSTI ID: 1547789

Report Number(s):: NREL/JA-5D00-72434
Journal ID: ISSN 0306-2619

Grant/Contract Number:: AC36-08GO28308; XGJ-6-62183-01

Resource Type:: Accepted Manuscript

Journal Name:: Applied Energy

Additional Journal Information:: Journal Volume: 238; Journal Issue: C; Journal ID: ISSN 0306-2619

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 17 WIND ENERGY; 24 POWER TRANSMISSION AND DISTRIBUTION; probabilistic wind forecasting; optimization; surrogate model; machine learning; pinball loss

Citation Formats


                    Sun, Mucun, Feng, Cong, Chartan, Erol Kevin, Hodge, Bri-Mathias S., and Zhang, Jie. A two-step short-term probabilistic wind forecasting methodology based on predictive distribution optimization.  United States: N. p., 2019. 
Web.  doi:10.1016/j.apenergy.2019.01.182.

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                    Sun, Mucun, Feng, Cong, Chartan, Erol Kevin, Hodge, Bri-Mathias S., & Zhang, Jie. A two-step short-term probabilistic wind forecasting methodology based on predictive distribution optimization.  United States.  https://doi.org/10.1016/j.apenergy.2019.01.182

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                    Sun, Mucun, Feng, Cong, Chartan, Erol Kevin, Hodge, Bri-Mathias S., and Zhang, Jie. Thu .  
"A two-step short-term probabilistic wind forecasting methodology based on predictive distribution optimization".  United States.  https://doi.org/10.1016/j.apenergy.2019.01.182.  https://www.osti.gov/servlets/purl/1494978.

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@article{osti_1494978,

  title        = {A two-step short-term probabilistic wind forecasting methodology based on predictive distribution optimization},

  author       = {Sun, Mucun and Feng, Cong and Chartan, Erol Kevin and Hodge, Bri-Mathias S. and Zhang, Jie},

  abstractNote = {With increasing wind penetrations into electric power systems, probabilistic wind forecasting becomes more critical to power system operations because of its capability of quantifying wind uncertainties. In this paper, a two-step probabilistic wind forecasting approach based on pinball loss optimization is developed. First, a multimodel machine learning-based ensemble deterministic forecasting framework is adopted to generate deterministic forecasts. The deterministic forecast is assumed to be the mean value of the predictive distribution at each forecasting time stamp. Then, the optimal unknown parameter (i.e., standard deviation) of the predictive distribution is estimated by a support vector regression surrogate model based on the deterministic forecasts. Lastly, probabilistic forecasts are generated from the predictive distribution. Numerical results of case studies at eight locations show that the developed two-step probabilistic forecasting methodology has improved the pinball loss metric score by up to 35% compared to a baseline quantile regression forecasting model.},

  doi          = {10.1016/j.apenergy.2019.01.182},

  journal      = {Applied Energy},

  number       = C,

  volume       = 238,

  place        = {United States},

  year         = {Thu Jan 31 00:00:00 EST 2019},

  month        = {Thu Jan 31 00:00:00 EST 2019}

}

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