Two-stage Short-term Wind Power Prediction with Multi-factor Joint Correction Considering Spatio-temporal Features

KONG Feng; ZHOU Chenlong; LI Zhixian

doi:10.19805/j.cnki.jcspe.2026.250143

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Two-stage Short-term Wind Power Prediction with Multi-factor Joint Correction Considering Spatio-temporal Features

更新时间：2026-06-11

- Two-stage Short-term Wind Power Prediction with Multi-factor Joint Correction Considering Spatio-temporal Features
- Chinese Journal of Power Engineering Vol. 46, Issue 6, Pages: 81-90(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2026.250143
  CLC： TM614
- Published：2026
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孔峰, 周辰龙, 李指贤. 考虑时空特征的两阶段多要素联合校正短期风能预测[J]. 动力工程学报, 2026, 46(6): 81-90.
孔峰, 周辰龙, 李指贤. 考虑时空特征的两阶段多要素联合校正短期风能预测[J]. 动力工程学报, 2026, 46(6): 81-90. DOI： 10.19805/j.cnki.jcspe.2026.250143.

DOI： 10.19805/j.cnki.jcspe.2026.250143

摘要

为解决传统预测模型对时空关联特征提取不足的问题

提出一种考虑时空特征融合与多要素误差校正的两阶段短期风能预测框架。首先

采用具有信号保真特性的辛几何包对原始风电序列去噪分解

获得连续的时序特征值并将其与风电机组运行序列整合为时序特征矩阵;其次

引入具有空间建模能力的图卷积神经网络

将时序特征矩阵与风电场空间图结构整合为时空矩阵;最后

利用牛顿-拉夫逊算法优化的Transformer-双向长短期记忆网络(bidirectional long short-term memory

BiLSTM)模型对时空矩阵进行预测。利用预测模型建立动态误差校正机制

通过误差预测值校正原始预测结果

形成具有自修正能力的预测系统。结果表明:该模型的均方根误差、平均绝对误差相较于其他各组合对比模型分别平均降低41.4%和36.4%

决定系数平均提升6%

在春、夏、秋、冬数据集及72 h持续预测中均表现最优

大幅度提升了风能预测的准确率

为风电并网提供了可靠支持。

Abstract

To address the issue of insufficient extraction of spatio-temporal correlation features by traditional prediction models

a two-stage short-term wind power prediction framework considering spatio-temporal feature fusion and multi-factor error correction was proposed. Firstly

the original wind power sequence was denoised and decomposed by symplectic geometry packet with signal fidelity characteristics

while continuous time-series feature values were obtained and integrated with the operation sequence of wind turbines to form a time-series feature matrix. Secondly

a graph convolutional network with spatial modeling capability was introduced

by which the time-series feature matrix and the spatial graph structure of the wind farm were integrated into a spatio-temporal matrix. Finally

the spatio-temporal matrix was predicted using Transformer-bidirectional long short-term memory(BiLSTM) model optimized by Newton-Raphson-based optimizer. A dynamic error correction mechanism was established through the prediction model

and the original prediction results were corrected based on the predicted error values

forming a prediction system with self-correction capabilities. Results demonstrate that the root mean square error and mean absolute error of this model are reduced by an average of 41.4% and 36.4%

respectively

compared with other combined comparison models

and the coefficient of determination is increased by an average of 6%. It performs optimally in the datasets of spring

summer

autumn

and winter

as well as in 72-hour continuous prediction

significantly improving the accuracy of wind energy prediction and providing reliable support for wind power grid connection.

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references

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