可再生能源2024，Vol.42Issue(4)：455-463,9.
应用PSO-RBF神经网络预测太阳能PV/T系统的热、电性能
Prediction of thermal and electrical performance of solar PV/T system using PSO-RBF neural network
摘要
Abstract
In order to accurately predict the thermal and electrical performance of solar photovoltaic/thermal(PV/T)systems,this study utilized the Particle Swarm Optimization(PSO)algorithm to optimize the Radial Basis Function(RBF)neural network.Based on this method,a simulation prediction model for the performance of solar PV/T systems was established and compared with a prediction model based on an unoptimized RBF neural network.Additionally,this research built a solar PV/T experimental platform and collected experimental data using a cloud platform for the aforementioned model.The research results indicate that the RBF neural network model optimized using the PSO algorithm exhibits better prediction accuracy compared to the unoptimized RBF neural network model.The optimized RBF neural network model demonstrates a 20%improvement in prediction accuracy and a 30%increase in prediction stability compared to the unoptimized model.The goodness of fit,as indicated by the R-value,is also improved compared to the unoptimized model.The prediction model established based on the PSO-RBF neural network can accurately predict the thermal and electrical performance of solar PV/T systems.关键词
PV/T/RBF神经网络/PSO算法/模拟预测Key words
PV/T/RBF neural network/PSO algorithm/simulation prediction分类
能源科技引用本文复制引用
何迪,王聪聪,陈红兵,孙俊辉,高雪宁,王传岭,马卓越..应用PSO-RBF神经网络预测太阳能PV/T系统的热、电性能[J].可再生能源,2024,42(4):455-463,9.基金项目
北京市科技计划项目(KM202010016012). （KM202010016012）
