Prediction of the effect of temperature on electric power in photovoltaic thermal systems based on natural zeolite plates

The present study aims to predict the effect of the panel temperature on the electrical power obtained by the photovoltaic thermal system (PVT) based on natural zeolite plates. It was carried out using the long short-term memory (LSTM) and multilayer feed forward (MLF) algorithms, which are popular regression-based deep learning methods. Models have been developed that can predict the effect of temperature on electrical power by using the regression data obtained from experimental measurements on the PVT system integrated with natural zeolite plates. In the study, a polycrystalline PVT panel was chosen, in the designed models, temperature, solar radiation, and photovoltaic panel temperature values were evaluated as input, and the amount of power to be produced as output. Mean absolute error, root mean square error (RMSE), mean absolute percentage error (MAPE), and R-2 functions were used for the prediction results of LSTM and MLF models designed with determined optimum hyperparameters. MAPE and R-2 values for long short-term memory (LSTM) and MLF algorithms were determined as 0.100 and 0.996, as well as 0.131 and 0.923, respectively. As can be understood from these values, the effect of the designed temperature on the PVT panel can be calculated with a very small margin of error with the LSTM algorithm.