现代信息科技2025，Vol.9Issue(24)：120-129,137,11.DOI:10.19850/j.cnki.2096-4706.2025.24.023
基于VMD-IKmeans-QLSTM的相似日光伏发电功率预测
Similar-day Photovoltaic Power Prediction Based on VMD-IKmeans-QLSTM
摘要
Abstract
Aiming at the problems of insufficient extreme weather samples and limited model expression ability in PV power prediction,a similar-day prediction method based on VMD-IKmeans-QLSTM is proposed.Firstly,a K-means clustering strategy with a multi-dimensional feature system is designed to ensure a sufficient number of extreme weather samples.Then,a multi-dimensional weighted feature matrix is constructed based on the Pearson correlation coefficient to realize accurate similar-day selection.The multi-scale decomposition of power signals is performed using Variational Mode Decomposition(VMD),and the Quantum Long and Short-Term Memory network(QLSTM)with quantum bit-variational quantum circuits is designed to utilize the quantum superposition state to enhance the nonlinear modeling capability for prediction.The application results of this combined model in a PV power station in Xinjiang show that the model improves R2 by 41.85%,8.06%and 48.46%in cloudy,sunny and rainy/snowy conditions,respectively,compared with the conventional model.关键词
光伏功率预测/聚类/相似日选取/变分模态分解/量子长短期记忆网络Key words
photovoltaic power prediction/clustering/similar-day selection/Variational Modal Decomposition/QLSTM分类
信息技术与安全科学引用本文复制引用
秦汉森,郭欢..基于VMD-IKmeans-QLSTM的相似日光伏发电功率预测[J].现代信息科技,2025,9(24):120-129,137,11.基金项目
江汉大学 2024 年研究生科研创新基金项目(KYCXJJ202443) （KYCXJJ202443）
