Mathematical and neural network models for predicting the electrical performance of a PV/T system

There are many photovoltaic/thermal (PV/T) systems' designs that are used mainly to reduce the temperature of the PV cell by using a thermal medium to cool the photovoltaic module. In this study, a PV/T system uses nano-phase change material (PCM) and nanofluid cooling system was adopted. Three cooling models were compared using nanofluid (SiC-water) and nano-PCM to improve the performance and productivity of the PV/T system. Three mathematical models were developed for linear prediction, and their results were compared with the predicted artificial neural network results, results were verified, and experimental results were appropriate. Three common evaluation criteria were adopted to compare that the results of proposed forecasting models with other models developed in many research studies are done, including the R2, mean square error (MSE), and root-mean-square error (RMSE). Besides, different experiments were implemented using varying number of hidden layers to ensure that the proposed neural network models achieved the best results. The best neural prediction models deployed in this study resulted in good R2 score of 0.81 and MSE of 0.0361 and RMSE and RMSE rate is 0.371. Mathematical models have proven their high potential to easily determine the future outcomes with the preferable circumstances for any PV/T system in a precise way to reduce the error rate to the lowest level.