Dual-channel Short-term Wind Power Forecasting Fusing Long- and Short-term Dependencies

GUO Lijin; LIANG Xiaolong; CAI Haoxiang

doi:10.19805/j.cnki.jcspe.2026.250492

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Dual-channel Short-term Wind Power Forecasting Fusing Long- and Short-term Dependencies

更新时间：2026-09-15

- Dual-channel Short-term Wind Power Forecasting Fusing Long- and Short-term Dependencies
- Chinese Journal of Power Engineering Vol. 46, Issue 9, Pages: 90-101(2026)
- 作者机构：
  
  1. 天津工业大学控制科学与工程学院,天津,300387
  2. 天津市电气装备智能控制重点实验室,天津,300387
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2026.250492
  CLC： TM614
- Published：2026
- 稿件说明：
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郭利进, 梁小龙, 蔡昊翔. 融合长短期依赖的双通道短期风电功率预测[J]. 动力工程学报, 2026, 46(9): 90-101.
郭利进, 梁小龙, 蔡昊翔. 融合长短期依赖的双通道短期风电功率预测[J]. 动力工程学报, 2026, 46(9): 90-101. DOI： 10.19805/j.cnki.jcspe.2026.250492.

DOI： 10.19805/j.cnki.jcspe.2026.250492

摘要

提出了一种基于时间卷积网络(TCN)的新型双通道风电功率预测模型。该模型采用双通道结构以实现风电场多源特征与模型计算的深度融合。时空感知通道通过级联多尺度熵路由注意力机制(MERA)与TCN结构，提取气象数据与风电功率之间的短期变化响应关系；时序特征通道则构建多步依赖时间卷积网络（MST），深入挖掘风电功率的长期时序依赖特征。融合后的双通道特征数据通过核感知网络（KAN）基于样条函数的非线性映射能力进行特征拟合，并输出最终预测结果。结果表明：模型各模块的必要性和有效性得到了验证；与其他基线预测模型相比，该模型具有性能优势。

Abstract

A novel dual-channel wind power forecasting model based on the temporal convolutional network (TCN) was proposed. This model adopted a dual-channel structure to achieve the deep fusion of multi-source features from wind farms and model computations. The spatiotemporal perception channel extracted the short-term variation response relationship between meteorological data and wind power by cascading a multi-scale entropy routing attention (MERA) mechanism with the TCN structure. Meanwhile

the temporal feature channel constructed a multi-step dependent temporal convolutional network (MST) to deeply mine the long-term temporal dependency features of wind power. The fused dual-channel feature data was fitted by a Kolmogorov-Arnold network (KAN)

utilizing the nonlinear mapping capability of its spline functions to output the final prediction results. Results show that the necessity and effectiveness of each module in the model are verified. Compared with other baseline forecasting models

the proposed model exhibits performance advantages.

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references

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