电力勘测设计Issue(7)：46-49,73,5.DOI:10.13500/j.dlkcsj.issn1671-9913.2025.07.007
基于遗传算法优化的变分模态分解与深度神经网络的光伏功率预测
Photovoltaic Power Forecasting Using Genetic Algorithm-Optimized Variational Mode Decomposition and Hybrid Deep Neural Networks
刘欲晓1
作者信息
- 1. 中国电力工程顾问集团国际工程有限公司,上海 200001
- 折叠
摘要
Abstract
Photovoltaic power prediction plays a crucial role in renewable energy management,as accurate forecasting significantly enhances grid stability and economic efficiency.This study proposes a hybrid photovoltaic power prediction method that integrates Genetic Algorithm(GA)-optimized Variational Mode Decomposition(VMD)with a Convolutional Neural Network-Gated Recurrent Memory Network(CNN-GRU).First,the genetic algorithm optimizes the key parameters of VMD to effectively decompose photovoltaic power sequences.Subsequently,the CNN extracts multi-scale local features,while the GRU captures long-term dependencies within the decomposed time series,thereby constructing a deep learning prediction model.Finally,the predictions from multiple components are integrated to generate the final output.Experimental results demonstrate that the proposed method outperforms traditional prediction approaches in accuracy,providing a novel and effective solution for photovoltaic power forecasting.关键词
光伏/功率预测/深度神经网络/门控循环网络Key words
photovoltaic(PV)/power prediction/deep neural network(DNN)/gated recurrent unit(GRU)分类
信息技术与安全科学引用本文复制引用
刘欲晓..基于遗传算法优化的变分模态分解与深度神经网络的光伏功率预测[J].电力勘测设计,2025,(7):46-49,73,5.
