Performance evaluation of solar photovoltaic/thermal system performance: An experimental and artificial neural network approach

A photovoltaic panel without cooling capabilities shows low efficiency and this efficiency deteriorates further during peak periods. In contrast, a photovoltaic/thermal (PV/T) system achieves higher electrical efficiency through a cooling mechanism that limits efficiency drops even during peak periods. This research centers on exploring the influence of temperature on the current, voltage, power, and efficiency of PV/T modules and the associated thermal system. Additionally, experimental investigations are conducted to examine the enhancement of PV/T power. Furthermore, the study involves the implementation of an Artificial Neural Network (ANN) for modeling the system, aiming to predict and analyze the performance of PV/T systems under the weather conditions prevalent in Oman and the Gulf region. Neural network models, such as multilayer perceptron (MLP), Second Order Volterra Model (SOVM), and Support Vector Machine (SVM) have been constructed to simulate and forecast the output power of the PV/T system. The SVM model demonstrated the lowest mean absolute percentage error (MAPE), registering at 0.0358, surpassing both Self-Organizing Feature Map (SOFM) and MLP with MAPE values of 0.0524 and 0.0601, respectively. In terms of root mean squared error (RMSE), SOFM exceeded MLP and SVM, yielding a lower error of 0.2235. However, when considering R2, MLP exhibited a notably high value of 0.9554, signifying a robust relationship. It is worth noting that R2 values approaching one indicate a strong correlation. The MLP model highlighted superior performance, achieving outstanding metrics with values of 99.67 %, 0.0631, and 0.2512 for R2, MAPE, and RMSE, respectively. This collective evidence solidifies the effectiveness and reliability of the proposed neural network models in predicting the PV/T system's output power.