融合多尺度周期性特征的光伏功率短期预测模型

陈万志; 刘昊洋; 王天元

doi:10.19912/j.0254-0096.tynxb.2025-0766

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融合多尺度周期性特征的光伏功率短期预测模型

更新时间：2026-10-15

- 融合多尺度周期性特征的光伏功率短期预测模型
- Vol. 47, Issue 9, Pages: 327-340(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2025-0766
  CLC： TM615
- Online First：10 October 2026，
  
  Published：2026
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陈万志, 刘昊洋, 王天元. 融合多尺度周期性特征的光伏功率短期预测模型[J]. 2026, 47(9): 327-340.
DOI：

陈万志, 刘昊洋, 王天元. 融合多尺度周期性特征的光伏功率短期预测模型[J]. 2026, 47(9): 327-340. DOI： 10.19912/j.0254-0096.tynxb.2025-0766.

摘要

针对光伏功率预测中存在的非线性、波动性及多尺度周期性特征建模不足的问题

提出一种融合多尺度周期性特征的光伏功率短期预测模型。首先

对原始数据进行预处理和皮尔逊相关系数特征重构

并利用门控聚合重分配机制优化相关因素交互;其次

通过构建周期-残差双阶段优化机制

借助残差循环预测技术获取残差分量和周期分量

并将残差分量送入Transformer编码器。此外

采用多尺度周期特征融合模块处理周期分量

结合时序适应性的多尺度卷积核以及基于时间维度均值化融合策略提升对周期模式理解能力;最后

对残差分量和周期分量分别建模后的输出结果进行整合得出预测结果。在3个真实数据集上的实验结果表明

所提模型在不同步长任务下均优于对比模型。在光伏数据集1中

相较于次优模型

其MAE和RMSE分别相对平均降低4.10%和3.98%;在光伏数据集2中

两项指标较次优分别相对平均降低3.31%和1.51%;在电力负荷数据集ETTh1中的短步长预测任务中

相较于次优模型

其MAE降低1.66%。此外

模型在多项任务中的决定系数均达到最优水平

证明其在短期光伏预测任务中的优越性能与泛化能力。

Abstract

In response to the problem of limitations in modeling nonlinearity

volatility

and multi-scale periodicity characteristics in photovoltaic power prediction

a short-term photovoltaic power prediction model using multi-scale periodic features was proposed. Firstly

the raw data are preprocessed and the Pearson correlation coefficient features are reconstructed

then cross-feature interactions are optimized through a gated aggregation redistribution mechanism. Secondly

a dual-phase periodicity-residual optimization mechanism is established

where residual components obtained through residual cycle forecasting are processed by Transformer encoders

while periodic components undergo multi-scale periodic feature fusion. The periodic processing module employs temporally adaptive multi-scale convolutional kernels and a time-dimension averaging fusion strategy to enhance periodic pattern recognition. Finally

predictions are generated through integrated outputs from both residual and periodic modeling paths. Experimental evaluations across three real datasets show that the proposed model outperforms comparison models across various horizons. Specifically

in PV Dataset 1

the average MAE and RMSE are reduced by 4.10% and 3.98% respectively compared to the second-best model; in PV Dataset 2

these indicators are reduced by 3.31% and 1.51%; in the short-term tasks of the ETTh1 dataset

the MAE is reduced by 1.66%. Additionally

the model achieves the highest in multiple tasks

verifying its superior predictive performance and generalization ability in short-term photovoltaic power prediction.

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