Adaptive ultra-short-term wind power prediction based on risk assessment

Yusheng Xue; Chen Yu; Kang Li; Fushuan Wen; Yi Ding; Qiuwei Wu; Guangya Yang

doi:10.17775/CSEEJPES.2016.00036

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Adaptive ultra-short-term wind power prediction based on risk assessment

更新时间：2025-12-18

- Adaptive ultra-short-term wind power prediction based on risk assessment
- Adaptive ultra-short-term wind power prediction based on risk assessment
- 中国电机工程学会电力与能源系统学报(英文) 2016年2卷第3期页码：59-64
- 作者机构：
  
  1. State Grid Electric Power Research Institute/NARI Group Corporation,Nanjing,China,211000
  2. Queen's University Belfast,, Belfast, GB,Belfast
  3. Zhejiang University,,, CN,Zhejiang,Hangzhou
  4. Danmarks Tekniske Universitet, Lyngby,DK
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2016.00036
  中图分类号：
- 纸质出版：2016
- 稿件说明：
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Yusheng Xue, Chen Yu, Kang Li, 等. Adaptive ultra-short-term wind power prediction based on risk assessment[J]. 中国电机工程学会电力与能源系统学报(英文), 2016,2(3):59-64.

Yusheng Xue, Chen Yu, Kang Li, et al. Adaptive ultra-short-term wind power prediction based on risk assessment[J]. CSEE Journal of Power and Energy Systems, 2016, 2(3): 59-64.
Yusheng Xue, Chen Yu, Kang Li, 等. Adaptive ultra-short-term wind power prediction based on risk assessment[J]. 中国电机工程学会电力与能源系统学报(英文), 2016,2(3):59-64. DOI： 10.17775/CSEEJPES.2016.00036.

Yusheng Xue, Chen Yu, Kang Li, et al. Adaptive ultra-short-term wind power prediction based on risk assessment[J]. CSEE Journal of Power and Energy Systems, 2016, 2(3): 59-64. DOI： 10.17775/CSEEJPES.2016.00036.

摘要

Abstract

A risk assessment based adaptive ultra-short-term wind power prediction (USTWPP) method is proposed in this paper. In this method

features are first extracted from the historical data

and then each wind power time series (WPTS) is split into several subsets defined by their stationary patterns. A WPTS that does not match any of the stationary patterns is then included in a subset of non-stationary patterns. Each WPTS subset is then related to a USTWPP model that is specially selected and optimized offline based on the proposed risk assessment index. For online applications

the pattern of the last short WPTS is first recognized

and the relevant prediction model is then applied for USTWPP. Experimental results confirm the efficacy of the proposed method.

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