Short-term Wind Power Integration Prediction Method Based on Error Correction

DING Tingting; YANG Ming; YU Yixiao; SI Zhiyuan; ZHANG Qiang

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Short-term Wind Power Integration Prediction Method Based on Error Correction

更新时间：2025-12-08

- Short-term Wind Power Integration Prediction Method Based on Error Correction
- Vol. 48, Issue 2, Pages: 488-496(2022)
- 作者机构：
  
  1. 山东大学电气工程学院
  2. 国网山东省电力公司山东电力调度控制中心
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published Online：28 February 2022，
  
  Published：2022
- 稿件说明：
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DING Tingting, YANG Ming, YU Yixiao, et al. Short-term Wind Power Integration Prediction Method Based on Error Correction[J]. 2022, 48(2): 488-496.
DOI：

DING Tingting, YANG Ming, YU Yixiao, et al. Short-term Wind Power Integration Prediction Method Based on Error Correction[J]. 2022, 48(2): 488-496. DOI：

摘要

为了提高短期风电功率预测精度，提出了一种基于误差修正的短期风电功率集成预测模型，此模型首先利用改进粒子群优化的极端梯度提升(extreme gradient boosting

XGBoost)初步建立风电功率预测模型，然后根据风速与功率的关系，将XGBoost模型预测误差分为低风速功率误差、中风速功率误差以及高风速功率误差3类，针对每类误差分别训练随机森林，得到对应的功率误差预测模型，最后将XGBoost模型预测结果和功率误差预测值相加即可得到基于误差修正的短期风电功率预测值。研究结果表明所提模型利用集成学习以及残差学习的方法提高了短期风电功率预测精度，因此所提模型可以促进风电消纳能力并提高电力系统运行的经济性。

Abstract

To improve the accuracy of short-term wind power prediction

a short-term wind power integrated prediction model based on error correction is proposed. A wind power prediction model is established by using the XGBoost model based on improved particle swarm optimization firstly. Based on the relationship between wind speed and power

the prediction error of the XGBoost model is divided into low wind speed power error

middle-speed power error

and high wind speed power error. The random forest is trained under each type of error

then the corresponding power error prediction model is obtained. The short-term wind power prediction results based on error correction can be obtained by the addition of XGBoost model prediction results and power error prediction results. The research results show that the proposed model improves the short-term wind power prediction accuracy through integrated learning and residual learning methods

therefore

the proposed model can promote the wind power consumption capacity and improve the economy of the power system.

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