基于时频域分解-增强-融合Transformer的多因素光伏发电功率预测

罗强; 高崇; 曹华珍; 何璇; 曾庆彬; 张勇军

doi:doi:10.19912/j.0254-0096.tynxb.2024-1912

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基于时频域分解-增强-融合Transformer的多因素光伏发电功率预测

更新时间：2026-04-08

- 基于时频域分解-增强-融合Transformer的多因素光伏发电功率预测
- Vol. 47, Issue 3, Pages: 604-615(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1912
  CLC： TM615
- Online First：07 April 2026，
  
  Published：2026
- 稿件说明：
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罗强, 高崇, 曹华珍, et al. 基于时频域分解-增强-融合Transformer的多因素光伏发电功率预测[J]. 2026, 47(3): 604-615.
DOI：

罗强, 高崇, 曹华珍, et al. 基于时频域分解-增强-融合Transformer的多因素光伏发电功率预测[J]. 2026, 47(3): 604-615. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1912.

摘要

针对现有光伏发电功率预测方法对于光伏发电功率变化曲线趋势性和随机性挖掘不足导致性能受限的问题

提出基于时频域分解-增强-融合Transformer的多因素光伏发电功率预测方法。首先

构建编码器-解码器模型

利用时域增强模块和频域增强模块

将光伏发电功率的历史和当前数据分别分解为周期项和趋势项;再通过时域和频域增强注意力模块挖掘当前数据和历史数据的语义关系。其次

采用随机层获取当前数据的随机项。然后

通过多级自适应的方式融合光伏发电功率数据的周期项、趋势项和随机项用于预测。此外

协同考虑太阳辐射、湿度、风速和温度等多因素

采用多变量通道独立的方式

降低数据冗余

进一步提升模型性能。仿真结果说明相比于其他方法

所提模型能够有效提高光伏发电功率的预测精度。

Abstract

A multi-factor photovoltaic power prediction method based on a time-frequency domain decomposition enhancement fusion Transformer is proposed to address the performance limitations caused by insufficient mining of the trend and randomness of the photovoltaic power change curve in existing photovoltaic power prediction methods. Firstly

an encoder-decoder framework is constructed

in which historical and current PV power data are decomposed into periodic and trend components through time-domain and frequency-domain enhancement modules. The semantic relationships between current and historical data are further extracted using time-domain and frequency-domain enhanced attention mechanisms. Subsequently

a stochastic layer is employed to capture the stochastic component of the current data. Then

a multi-level adaptive fusion strategy integrates the periodic

trend

and stochastic components of PV power data for forecasting. In addition

multiple factors such as solar irradiance

humidity

wind speed

and temperature are incorporated using an independent multi-channel feature extraction approach to reduce data redundancy and further improve model performance. Experiments on PVGD-1 and DKASC-Sanyo datasets show that the proposed method outperforms Informer

Autoformer

and Fedformer.

关键词

Keywords

references

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