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
何迪 1王聪聪 1陈红兵 1孙俊辉 2高雪宁 1王传岭 1马卓越3
作者信息
- 1. 北京建筑大学 环境与能源工程学院 供热供燃气通风及空调工程北京市重点实验室,北京 100044
- 2. 中国建筑第六工程局有限公司,天津 300012
- 3. 同圆设计集团股份有限公司,山东 济南 250024
- 折叠
摘要
为准确预测太阳能光伏光热(Solar Photovoltaic/Thermal,PV/T)系统的热、电性能,文章利用PSO(Particle Swarm Optimization)算法优化了RBF(Radial Basis Function)神经网络,并基于此方法建立了太阳能PV/T系统性能的仿真预测模型,与基于未优化RBF神经网络建立的预测模型进行了对比分析.同时,搭建了太阳能PV/T实验平台,通过云平台采集实验数据用于上述模型.研究结果表明:使用PSO算法优化后的RBF神经网络模型相较于未优化模型预测精度提高了 20%,预测稳定性提高了 30%,拟合优度R值有所提升.基于PSO-RBF神经网络建立的预测模型可精确预测太阳能PV/T系统的热、电性能.
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引用本文复制引用
基金项目
北京市科技计划项目(KM202010016012)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据