Short-term Power Prediction of Wind Power Cluster Based on SDAE Deep Learning and Multiple Integration

LI Cong; PENG Xiaosheng; WANG Haohuai; CHE Jianfeng; WANG Bo; LIU Chun

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Short-term Power Prediction of Wind Power Cluster Based on SDAE Deep Learning and Multiple Integration

更新时间：2025-12-08

- Short-term Power Prediction of Wind Power Cluster Based on SDAE Deep Learning and Multiple Integration
- Vol. 48, Issue 2, Pages: 504-512(2022)
- 作者机构：
  
  1. 华中科技大学电气与电子工程学院强电磁工程与新技术国家重点实验室
  2. 中国南方电网电力调度控制中心
  3. 中国电力科学研究院有限公司新能源与储能运行控制国家重点实验室
- 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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LI Cong, PENG Xiaosheng, WANG Haohuai, et al. Short-term Power Prediction of Wind Power Cluster Based on SDAE Deep Learning and Multiple Integration[J]. 2022, 48(2): 504-512.
DOI：

LI Cong, PENG Xiaosheng, WANG Haohuai, et al. Short-term Power Prediction of Wind Power Cluster Based on SDAE Deep Learning and Multiple Integration[J]. 2022, 48(2): 504-512. DOI：

摘要

风电功率预测(wind power prediction

WPP)技术是电力系统调度与安全运行的关键性因素，为了更好地提升风电功率预测技术的精度，在集成学习的基础上提出了一种多重集成的集群短期WPP方法。所提方法包含4步：第1步，利用变分模式分解、经验模态分解和小波变换将原始风电序列分解为多个子序列；第2步，根据子序列构造多个堆叠去噪自动编码器(stacked denoising autoencoders

SDAE)进行深度学习；第3步，将第2步的结果随机划分成几个集合，利用支持向量机(support vector machine

SVM)对每个集合进行集成；第4步，将第3步的集成的结果再随机划分成几个集合，利用SVM对每个集合进行集成，重复以上步骤直至得到最终的集成预测结果。结果表明，多重集成学习得到前96 h预测结果的平均归一化均方根误差相比单次集成减少了0.010 1，百分比为9.01%；相比SDAE减少了0.015 1，百分比为13.54%；相比SVM减少了0.017 5，百分比为14.66%。论文研究可为基于深度学习和集成学习的风电集群短期功率预测提供参考。

Abstract

Wind power prediction（WPP） technology is a key factor in power system scheduling and safe operation. In order to better improve the accuracy of the WPP technology

this paper proposes a multi-integrated cluster short-term WPP method based on ensemble learning

which includes four steps. The first step is to decompose the original wind power sequence into multiple sub-sequences by using variational mode decomposition

empirical mode decomposition and wavelet transform. The second step is to construct multiple stacked denoising autoencoders（SDAEs） based on subsequences for deep learning. The third step is to randomly divide the results of the second step into several sets

and integrate each set using the support vector machine（SVM）. The fourth step is to randomly divide the integration results of the third step into several sets

integrate each set with SVM

and repeat the above steps until the final integration prediction result is obtained. The results show that the average normalized root mean square error（RMSE） of the first 96 h prediction results obtained by multiple integration learning is reduced by 0.010 1（9.01%） compared with that obtained by single integration learning. Compared with SDAE

it decreases by 0.015 1

and the percentage is 13.54%. Compared with SVM

it decreases by 0.017 5

a percentage of 14.66%. This paper can provide reference for short term power prediction of wind power cluster based on deep learning and ensemble learning.

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Related Author

CHENG Kai

PENG Xiaosheng

XU Qiyou

WANG Bo

LIU Chun

CHE Jianfeng

ZHU Lingyu

TANG Yizheng

Related Institution

State Key Laboratory of Operation and Control of Renenable Energy & Storage Systems,China Electric Power Research Institute

State Key Laboratory of Advanced Electromagnetic Engineering and Technology,School of Electrical and Electronic Engineering,Huazhong University of Science and Technology

Center for Power Electronics Systems (CPES), Virginia Tech, Blacksburg

State Key Laboratory of Electrical Insulation and Power Equipment(Xi'an Jiaotong University)

China South Power Grid International Co., Ltd.

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