基于BO-SVM和ISO改进的ENN光伏功率超短期预测模型

王育飞; 汪弦哲; 薛花; 余光正; 杨秀

doi:doi:10.19912/j.0254-0096.tynxb.2024-0180

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基于BO-SVM和ISO改进的ENN光伏功率超短期预测模型

更新时间：2025-12-02

- 基于BO-SVM和ISO改进的ENN光伏功率超短期预测模型
- Vol. 46, Issue 6, Pages: 280-288(2025)
- 作者机构：
- 作者简介：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-0180
  CLC： TM615
- Published Online：07 July 2025，
  
  Published：2025
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王育飞, 汪弦哲, 薛花, et al. 基于BO-SVM和ISO改进的ENN光伏功率超短期预测模型[J]. 2025, 46(6): 280-288.
DOI：

王育飞, 汪弦哲, 薛花, et al. 基于BO-SVM和ISO改进的ENN光伏功率超短期预测模型[J]. 2025, 46(6): 280-288. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0180.

摘要

针对超短期光伏功率预测时传统大脑情绪神经网络（ENN）短反射通路拟合能力不强导致预测精度不高的问题

提出一种基于贝叶斯优化-支持向量机（BO-SVM）和改进蛇优化（ISO）的大脑情绪神经网络光伏功率预测模型。首先

为提高短反射通路的非线性拟合能力

采用基于BO-SVM的历史数据三维相点分类平面选取方法

并考虑三维相点到分类平面距离

提取历史数据非线性特征;其次

改进蛇优化算法并用于ENN的权值寻优

确保短反射通路合理表达历史数据非线性特征;然后

对光伏功率时间序列进行混沌相空间重构

并建立基于BO-SVM和ISO改进的ENN光伏功率超短期预测模型;最后

运用实测数据

验证所提模型实现不同天气下光伏功率超短期预测精度的提升。

Abstract

Aiming at the problem of low accuracy in ultra-short-term photovoltaic power forecasting

caused by weak fitting ability of short reflex pathway of traditional brain emotional neural network （ENN）

a modified model based on ENN using bayesian optimization-support vector machine （BO-SVM） and improved snake optimization （ISO） is proposed. Firstly

in order to improve the nonlinear fitting ability of short reflen pathway

the hyperplane selection method of three-dimension phase points of historical data based on BO-SVM is adopted

and the nonlinear features of historical data are extracted by considering the distance between three-dimension phase points and hyperplane. Secondly

the snake optimization algorithm is improved and applied to the weight optimization of ENN

to ensure the short reflen pathway can reasonably express the nonlinear characteristics of the historical data. Then

the chaos phase space reconstruction of photovoltaic power time series is carried out

and the ultra-short-term forecasting model of photovoltaic power based on improved ENN using BO-SVM and ISO is established. Finally

the proposed ultra-short term forecasting model of photovoltaic power is verified using the measured data

which can realize the improvement of accuracy under different weather conditions.

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Keywords

references

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