基于HGS-VMD-ENN的光伏发电功率超短期混沌预测模型

王育飞; 吴顾轩; 桑一岩; 薛花; 于艾清; 米阳

doi:doi:10.19912/j.0254-0096.tynxb.2024-1485

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基于HGS-VMD-ENN的光伏发电功率超短期混沌预测模型

更新时间：2026-02-06

- 基于HGS-VMD-ENN的光伏发电功率超短期混沌预测模型
- Vol. 47, Issue 1, Pages: 64-71(2026)
- 作者机构：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1485
  CLC： TM615
- Online First：05 February 2026，
  
  Published：05 February 2026
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王育飞, 吴顾轩, 桑一岩, et al. 基于HGS-VMD-ENN的光伏发电功率超短期混沌预测模型[J]. 2026, 47(1): 64-71.
DOI：

王育飞, 吴顾轩, 桑一岩, et al. 基于HGS-VMD-ENN的光伏发电功率超短期混沌预测模型[J]. 2026, 47(1): 64-71. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1485.

摘要

针对非晴空天气下光伏发电功率剧烈波动导致的预测准确度不足问题

提出一种基于饥饿游戏搜索算法（HGS）优化变分模态分解（VMD）和大脑情绪神经网络（ENN）的光伏发电功率超短期混沌预测模型。首先

为提高VMD自适应性

将HGS算法用于VMD核心参数寻优

并设计一种考虑加权排列熵和分解损失的HGS-VMD适应度函数

降低分解分量的复杂性和残差分量对预测结果的影响。其次

采用改进C-C法对VMD分解分量重构系统相空间

并将相空间重构矩阵输入ENN模型进行单步滚动预测。最后

基于实测光伏发电功率数据对所提预测模型进行仿真验证

结果表明所提预测模型能有效提高光伏发电功率在非晴空天气下的预测准确度。

Abstract

Aiming at the problem of insufficient prediction accuracy due to the drastic fluctuation of PV power under non-clear-skg weather conditions

an ultra-short-term PV power chaotic prediction model based on hunger games search （HGS） optimized variational mode decomposition （VMD） and emotional neural network （ENN） is proposed. Firstly

the HGS algorithm is used for optimization of VMD core parameters to improve the adaptivity of VMD

and an HGS-VMD fitness function considering weighted permutation entropy and decomposition loss is designed to reduce the complexity of the decomposition component and the influence of the residual component on the prediction results. Then

the phase space is reconstructed using the improved C-C method for the VMD decomposition components

and after extracting their regularity information

the phase space reconstruction matrix is input into the ENN model for prediction. Finally

the proposed prediction model is verified by simulation based on the measured PV power data

and the results show that the proposed prediction model can effectively improve the prediction accuracy of PV power under non-clear-sky weather conditions.

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references

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