Prediction of thermal and electrical performance of solar PV/T system using PSO-RBF neural network
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.
国家科技期刊平台
NSTL
万方数据
维普
