Enhancing solar panel efficiency with LSTM-based MPPT controllers

Abstract

This paper investigates the application of Long Short-Term Memory (LSTM) neural networks as Maximum Power Point Tracking (MPPT) controllers for solar panels. Traditional MPPT algorithms, including Perturb and Observe (P&O), Incremental Conductance (IncCond), and Hill Climbing (HC), are compared with LSTM-based approaches in terms of accuracy, efficiency, and adaptability. Minute-level data on voltage, current, power output, temperature, and solar irradiance from diverse locations are used to train and evaluate the LSTM model. Results demonstrate that LSTM-based MPPT controllers outperform traditional algorithms, offering superior tracking accuracy and adaptability to dynamic environmental conditions. The study highlights the significance of LSTM-based controllers in enhancing solar panel efficiency and maximizing energy harvesting. This research contributes to the advancement of renewable energy technologies and underscores the potential of artificial intelligence in optimizing solar energy systems.