Comparison of deep learning and regression-based MPPT algorithms in PV systems

Solar energy systems (SES) and photovoltaic (PV) modules should be operated at the maximum power point (MPP) to achieve highest efficiency in generation processes. Maximum tracking (MPPT) applications using conventional methods may not able follow global MPP (GMPP) of PV system under changing atmospheric conditions they could oscillate around local MPP. In this study, a machine learning deep (DL) based long short-term memory (LSTM) model is proposed as an innovative solution for MPPT. Contrary traditional MPPT current voltage sensors, output resistance module estimation was made by environmental parameters (such temperature radiation) artificial intelligence algorithms study.The LSTM compared with neural networks (ANN) regression regarding mean square error (MSE), root error(RMSE) absolute (MAE) parameters. It has been determined that better performance more successfully other methods. Finally, after comparison ANN method, it proved gives 37%, 21%, 31% successful MSE, RMSE, MAE results, respectively.