CNN-LSTM to Predict and Investigate the Performance of a Thermal/Photovoltaic System Cooled by Nanofluid (Al2O3) in a Hot-Climate Location

Abstract

The proposed study aims to estimate and conduct an investigation of the performance of a hybrid thermal/photovoltaic system cooled by nanofluid (Al2O3) utilizing time-series deep learning networks. The use of nanofluids greatly improves the proposed system’s performance deficiencies due to the rise in cell temperature, and time-series algorithms assist in investigating its potential in various regions more accurately. In this paper, energy balance methods were used to generate the hybrid thermal/photovoltaic system’s performance located in Tabuk, Saudi Arabia. Moreover, the generated dataset for the hybrid thermal/photovoltaic system was utilized to develop deep learning algorithms, such as the hybrid convolutional neural network (CNN) and long short-term memory (LSTM), in order to estimate and investigate the thermal/photovoltaic performance. The models were evaluated based on several performance metrics such as mean absolute percentage error (MAPE), root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). The results of the evaluated algorithms were compared and provided high accuracy ranges of 98.3–99.3%. It was observed that the best model among the others was CNN-LSTM, with an MAE of 0.375. The model was utilized to investigate the electrical and thermal performance of the hybrid thermal/photovoltaic application cooled by Al2O3 in addition to the hybrid thermal/photovoltaic cell temperature. The results show hybrid thermal/photovoltaic cell temperatures could be decreased to 43 °C, while the average daily thermal and electrical efficiencies were raised by 15% and 9%, respectively.