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Prediction of thermal and electrical performance of solar PV/T system using PSO-RBF neural network
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Di He1, Congcong Wang1, Hongbing Chen1, Junhui Sun2, Xuening Gao1, Chuanling Wang1, Zhuoyue Ma3
Renewable Energy Resources | 2024, 42(4) : 455 - 463
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Renewable Energy Resources | 2024, 42(4): 455-463
Prediction of thermal and electrical performance of solar PV/T system using PSO-RBF neural network
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Di He1, Congcong Wang1, Hongbing Chen1, Junhui Sun2, Xuening Gao1, Chuanling Wang1, Zhuoyue Ma3
Affiliations
  • 1 School of Environment and Energy Engineering, Beijing Municipal Key Lab of HVAC Beijing University of Civil Engineering and Architecture Beijing 100044 China
  • 2 China Construction Sixth Engineering Bureau Crop., Ltd Tianjin 300012 China
  • 3 Tong Yuan Design Group Co., Ltd Ji'nan 250024 China
Published: 2024-04-20
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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 Rvalue, is also improved compared to the unoptimized model. The prediction model established based on the PSORBF neural network can accurately predict the thermal and electrical performance of solar PV/T systems.

PV/T  /  RBF neural network  /  PSO algorithm  /  simulation prediction
Di He, Congcong Wang, Hongbing Chen, Junhui Sun, Xuening Gao, Chuanling Wang, Zhuoyue Ma. Prediction of thermal and electrical performance of solar PV/T system using PSO-RBF neural network[J]. Renewable Energy Resources, 2024 , 42 (4) : 455 -463 .
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Year 2024 volume 42 Issue 4
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Article Info
  • Receive Date：2023-03-31
  • Online Date：2025-07-22
  • Published：2024-04-20
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  • Received：2023-03-31
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    1 School of Environment and Energy Engineering, Beijing Municipal Key Lab of HVAC Beijing University of Civil Engineering and Architecture Beijing 100044 China
    2 China Construction Sixth Engineering Bureau Crop., Ltd Tianjin 300012 China
    3 Tong Yuan Design Group Co., Ltd Ji'nan 250024 China
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表12种不同金属材料的力学参数
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Number of
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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