Wind power prediction based on PCA and SOFTS fusion model

CHEN Zhongzhong; GENG Xiaofei; DONG Xiangming; LI Lianghao; HU Jiawei; WU Congwen; KANG Fuquan

doi:10.11930/j.issn.1004-9649.202510044

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Wind power prediction based on PCA and SOFTS fusion model

更新时间：2026-07-01

- Wind power prediction based on PCA and SOFTS fusion model
- Vol. 59, Issue 6, Pages: 125-132(2026)
- 作者机构：
  
  1. 国家电网有限公司华中分部,湖北,武汉,430077
  2. 国网电力科学研究院有限公司（南瑞集团有限公司）,江苏,南京,211106
- 作者简介：
- 基金信息：
- DOI：10.11930/j.issn.1004-9649.202510044
  CLC：
- Online First：22 June 2026，
  
  Published：2026
- 稿件说明：
移动端阅览
陈钟钟, 耿小飞, 董向明, 李良浩, 胡加伟, 吴从文, 康福权. 基于PCA和SOFTS融合模型的风电功率预测[J]. 中国电力, 2026, 59(6): 125-132.

CHEN Zhongzhong, GENG Xiaofei, DONG Xiangming, et al. Wind power prediction based on PCA and SOFTS fusion model[J]. 2026, 59(6): 125-132.
陈钟钟, 耿小飞, 董向明, 李良浩, 胡加伟, 吴从文, 康福权. 基于PCA和SOFTS融合模型的风电功率预测[J]. 中国电力, 2026, 59(6): 125-132. DOI： 10.11930/j.issn.1004-9649.202510044.

CHEN Zhongzhong, GENG Xiaofei, DONG Xiangming, et al. Wind power prediction based on PCA and SOFTS fusion model[J]. 2026, 59(6): 125-132. DOI： 10.11930/j.issn.1004-9649.202510044.

摘要

为了提升非平稳风电功率序列预测准确性和鲁棒性，提出一种基于主成分分析（principal component analysis，PCA）和序列核心融合时间序列预测器（series-core fused time series forecaster，SOFTS）融合的风电功率预测方法。首先，采用PCA计算影响风电功率变量主成分构造模型特征变量，将其输入SOFTS多层感知机进行风电功率相关变量序列编码，基于星型聚合再分配模块获取每个输入序列的核心表示；然后，再与局部风电功率相关变量序列表示深度融合，以横向和纵向交叉方式有效捕捉通道间复杂相关性和全局特征信息；最后，通过训练SOFTS网络参数，建立基于PCA和SOFTS融合的风电功率预测模型。采用某地区风电场实际运行数据验证，所提风电功率预测模型预测准确率为98.38%，与其他模型相比，具有更高的预测准确率和对非平稳序列更强的鲁棒性。

Abstract

In order to improve the prediction accuracy and robustness of non-stationary wind power series

a wind power prediction method based on principal component analysis (PCA) and series-core fused time series forecaster (SOFTS) is proposed in this paper. Firstly

PCA is used to calculate the principal components of wind power variables to construct model feature variables

which are input into the SOFTS multi-layer perceptron for wind power related variables sequence encoding. The core representation of each input sequence is obtained based on the spatiotemporal aggregation and redistribution module. Secondly

complex correlations and global feature information between channels are captured effectively through horizontal and vertical intersections. Finally

by training the SOFTS network parameters

a wind power prediction model based on PCA and SOFTS fusion is established. The actual operation data of a wind farm in a certain area is used for verification. The prediction accuracy of the proposed wind power prediction model is 98.38%. Compared with other models

it has higher prediction accuracy and stronger robustness to non-stationary sequences.

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references

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