基于SVMD-IDBO-KELM的短期光伏发电功率预测

吴艳娟; 荣旺; 郭玥; 叶技松

doi:doi:10.19912/j.0254-0096.tynxb.2024-0292

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基于SVMD-IDBO-KELM的短期光伏发电功率预测

更新时间：2025-12-02

- 基于SVMD-IDBO-KELM的短期光伏发电功率预测
- Vol. 46, Issue 6, Pages: 271-279(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-0292
  CLC： TM615
- Published Online：07 July 2025，
  
  Published：2025
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吴艳娟, 荣旺, 郭玥, et al. 基于SVMD-IDBO-KELM的短期光伏发电功率预测[J]. 2025, 46(6): 271-279.
DOI：

吴艳娟, 荣旺, 郭玥, et al. 基于SVMD-IDBO-KELM的短期光伏发电功率预测[J]. 2025, 46(6): 271-279. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0292.

摘要

为提升不同天气条件下短期光伏发电功率预测的准确性

提出一种基于逐次变分模态分解（SVMD）和改进蜣螂优化算法（IDBO）优化核极限学习机（KELM）的预测模型。首先

通过高斯混合模型将数据集划分成不同天气类型下的相似日样本;其次

通过SVMD将数据集进行模态分解

得到相对平稳的子序列来改善数据质量;之后

运用IDBO对KELM进行改进

构建IDBO-KELM预测模型

并对不同子序列进行预测;最后

通过重组各子序列的预测值得到最终的预测结果。实验结果表明：该方法在3种不同的天气类型下均可取得良好的预测结果

并且比其他模型的预测精度更高。

Abstract

To improve the accuracy of short-term photovoltaic power forecasting under various weather conditions

this paper introduces a model that combines Successive Variational Mode Decomposition （SVMD） and an Improved Dung Beetle Optimization algorithm （IDBO） to optimize a Kernel Extreme Learning Machine （KELM）. Firstly

the dataset is categorized into similar daily samples for different weather types using a Gaussian Mixture Model. Then

SVMD is applied to decompose the dataset into stable subsequences

thereby enhancing data quality. Next

IDBO is employed to optimize KELM

resulting in an IDBO-KELM prediction model specifically tailored for forecasting these subsequences. Finally

the predicted values from the subsequences are recombined to produce the final forecast. Experimental results demonstrate that this method performs well under three different weather conditions and exhibits superior accuracy when compared to other models.

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