Short-term wind power prediction basedon extreme learning machine with errorcorrection

Zhi Li; Lin Ye; Yongning Zhao; Xuri Song; Jingzhu Teng; Jingxin Jin

doi:10.1186/s41601-016-0016-y

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Short-term wind power prediction basedon extreme learning machine with errorcorrection

Contents | 更新时间：2026-06-25

- Short-term wind power prediction basedon extreme learning machine with errorcorrection
- Short-term wind power prediction basedon extreme learning machine with errorcorrection
- 现代电力系统保护与控制（英文） 2016年1卷第1期
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- DOI：10.1186/s41601-016-0016-y
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- 纸质出版：2016
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Zhi Li, Lin Ye, Yongning Zhao, 等. Short-term wind power prediction basedon extreme learning machine with errorcorrection[J]. 现代电力系统保护与控制（英文）, 2016,1(1).

Zhi Li, Lin Ye, Yongning Zhao, et al. Short-term wind power prediction basedon extreme learning machine with errorcorrection[J]. Protection and Control of Modern Power Systems, 2016, 1(1).
Zhi Li, Lin Ye, Yongning Zhao, 等. Short-term wind power prediction basedon extreme learning machine with errorcorrection[J]. 现代电力系统保护与控制（英文）, 2016,1(1). DOI： 10.1186/s41601-016-0016-y.

Zhi Li, Lin Ye, Yongning Zhao, et al. Short-term wind power prediction basedon extreme learning machine with errorcorrection[J]. Protection and Control of Modern Power Systems, 2016, 1(1). DOI： 10.1186/s41601-016-0016-y.

摘要

Introduction: Large-scale integration of wind generation brings great challenges to the secure operation of the power systems due to the intermittence nature of wind. The fluctuation of the wind generation has a great impact on the unit commitment. Thus accurate wind power forecasting plays a key role in dealing with the challenges of power system operation under uncertainties in an economical and technical way. Methods: In this paper

a combined approach based on Extreme Learning Machine (ELM) and an error correction model is proposed to predict wind power in the short-term time scale. Firstly an ELM is utilized to forecast the short-term wind power. Then the ultra-short-term wind power forecasting is acquired based on processing the short-term forecasting error by persistence method. Results: For short-term forecasting

the Extreme Learning Machine (ELM) doesn’t perform well. The overall NRMSE (Normalized Root Mean Square Error) of forecasting results for 66 days is 21.09 %. For the ultra-short term forecasting after error correction

most of forecasting errors lie in the interval of [−10 MW

10 MW]. The error distribution is concentrated and almost unbiased. The overall NRMSE is 5.76 %. Conclusion: The ultra-short-term wind power forecasting accuracy is further improved by using error correction in terms of normalized root mean squared error (NRMSE).

Abstract

most of forecasting errors lie in the interval of [−10 MW

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