融合经验模态分解与特征增强的Transformer-TCN光伏功率预测模型

张海清; 刘航; 唐鑫; 杨孟辑; 于曦; 李代伟

doi:10.19912/j.0254-0096.tynxb.2025-0685

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融合经验模态分解与特征增强的Transformer-TCN光伏功率预测模型

更新时间：2026-10-15

- 融合经验模态分解与特征增强的Transformer-TCN光伏功率预测模型
- Vol. 47, Issue 9, Pages: 203-211(2026)
- 作者机构：
- 作者简介：
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- DOI：10.19912/j.0254-0096.tynxb.2025-0685
  CLC： TP18
- Online First：10 October 2026，
  
  Published：2026
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张海清, 刘航, 唐鑫, et al. 融合经验模态分解与特征增强的Transformer-TCN光伏功率预测模型[J]. 2026, 47(9): 203-211.
DOI：

张海清, 刘航, 唐鑫, et al. 融合经验模态分解与特征增强的Transformer-TCN光伏功率预测模型[J]. 2026, 47(9): 203-211. DOI： 10.19912/j.0254-0096.tynxb.2025-0685.

摘要

针对单一深度学习模型难以兼顾长短期依赖与局部特征波动的问题

提出一种基于经验模态分解（EMD）、Transformer和时间卷积网络（TCN）的双通道耦合预测模型。模型利用EMD将原始光伏序列分解为一组本征模态函数（IMF）分量

针对不同平稳性的IMF分量

分别采用基于残差网络改进的Transformer模型（Enresformer）和基于门控网络改进的TCN模型进行预测

并集成预测结果。引入误差修正机制以进一步提升预测精度。基于多个实际光伏数据集的对比实验表明

所提模型在预测精度方面显著优于当前主流的深度学习模型和耦合模型

为功率预测提供了一种高效且可靠的解决方案。

Abstract

Photovoltaic （PV） power generation

as a crucial source of clean energy

requires accurate power forecasting to facilitate efficient power dispatch. PV power is highly influenced by meteorological factors and exhibits significant intermittency and volatility. Traditional time series forecasting models often fail to capture the dynamic correlations among meteorological factors

leading to limited forecasting accuracy. Meanwhile

single deep learning architectures struggle to effectively capture the intricate interplay between multi-scale temporal dependencies and localized feature variations. To address these challenges

this paper proposes a dual-channel hybrid forecasting model integrating empirical mode decomposition （EMD）

Transformer

and temporal convolutional network （TCN）. EMD is employed to decompose the raw PV power signal into a set of intrinsic mode functions （IMFs）. The stationarity of each IMF component is quantified using sample entropy （SE）

guiding the selection of tailored predictive models for components with distinct stationarity characteristics. An enhanced residual transformer （Enresformer）

which incorporates residual networks

and a gated network-enhanced TCN model are employed to forecast these components

with subsequent ensembling of their outputs. An error correction mechanism is introduced to further refine prediction accuracy. Comparative experiments based on multiple real-world PV datasets show that the proposed model significantly outperforms existing deep learning and hybrid models in terms of forecasting accuracy

providing an efficient and reliable solution for PV power prediction in practical power stations.

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Keywords

references

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