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Title: Nonparametric Prediction Intervals of Wind Power via Linear Programming

Abstract

This letter proposes a machine learning-based linear programming model that quickly establishes the nonparametric prediction intervals of wind power by integrating extreme learning machine and quantile regression. The proportions of quantiles can be adaptively determined via sensitivity analysis. The proposed method has been proven to be significantly efficient and reliable, with a high application potential in power systems.

Authors:
ORCiD logo; ORCiD logo; ; ; ORCiD logo
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability (OE)
OSTI Identifier:
1464765
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
IEEE Transactions on Power Systems
Additional Journal Information:
Journal Volume: 33; Journal Issue: 1; Journal ID: ISSN 0885-8950
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
extreme learning machine; forecasting; linear programming; prediction interval; sensitivity analysis; wind power

Citation Formats

Wan, Can, Wang, Jianhui, Lin, Jin, Song, Yonghua, and Dong, Zhao Yang. Nonparametric Prediction Intervals of Wind Power via Linear Programming. United States: N. p., 2018. Web. doi:10.1109/tpwrs.2017.2716658.
Wan, Can, Wang, Jianhui, Lin, Jin, Song, Yonghua, & Dong, Zhao Yang. Nonparametric Prediction Intervals of Wind Power via Linear Programming. United States. doi:10.1109/tpwrs.2017.2716658.
Wan, Can, Wang, Jianhui, Lin, Jin, Song, Yonghua, and Dong, Zhao Yang. Mon . "Nonparametric Prediction Intervals of Wind Power via Linear Programming". United States. doi:10.1109/tpwrs.2017.2716658.
@article{osti_1464765,
title = {Nonparametric Prediction Intervals of Wind Power via Linear Programming},
author = {Wan, Can and Wang, Jianhui and Lin, Jin and Song, Yonghua and Dong, Zhao Yang},
abstractNote = {This letter proposes a machine learning-based linear programming model that quickly establishes the nonparametric prediction intervals of wind power by integrating extreme learning machine and quantile regression. The proportions of quantiles can be adaptively determined via sensitivity analysis. The proposed method has been proven to be significantly efficient and reliable, with a high application potential in power systems.},
doi = {10.1109/tpwrs.2017.2716658},
journal = {IEEE Transactions on Power Systems},
issn = {0885-8950},
number = 1,
volume = 33,
place = {United States},
year = {2018},
month = {1}
}