Neural Network Modeling of the Solar Photovoltaic Thermal Module Performance

Abstract

The design of photovoltaic thermal modules (PVT modules) features a nonuniform distribution of coolant temperature in the channel, and the solar cells (SC) that are in thermal contact with the PVT module channel are under different temperature conditions. The nonuniform distribution of the SC temperature causes undesirable effects that are complex and nonlinear, such as a drop of generated power and SC damage resulting from the occurrence of hot spots. The aim of the work is to develop a mathematical model for modeling the PVT module thermal and electrical performance based on a feedforward artificial neural network (FNN). A two-layer FNN with sigmoid hidden neurons and linear output neurons has been developed. The input layer is made up of the ambient temperature, coolant flowrate and environmental variables (the total insolation). The output layer represents the PVT module thermal and electrical efficiencies. The developed FNN was trained and adapted on the basis of modeling and an experimental database on the PVT module input and output parameters. The developed FNN was trained using the Levenberg–Marquardt algorithm. The mean absolute error achieved during the training is in the range from –0.319 to 0.448 for electrical efficiency and from –0.129 to 0.198 for thermal efficiency. The r.m.s error is 0.0678 for electrical efficiency and 0.0247 for thermal efficiency; the training time is 15 s or longer. An effective model has been developed that implements a new approach to modeling the performance of PVT modules based on artificial neural network algorithms with fairly close values of thermal and electrical efficiencies.