Integrating AI and Energy Systems: LSTM Model for Photovoltaic Performance Prediction

In the past decade, solar photovoltaics (PV) have emerged as a key technology in the global shift toward renewable energy, addressing critical issues like climate change and energy security. Despite their growing adoption, PV systems face significant challenges, including efficiency degradation due to environmental factors (e.g., dust, shading, temperature fluctuations) and the need for effective fault detection and predictive maintenance. Traditional monitoring techniques, such as remote sensing, thermal imaging, and regression-based models, have progressed but often fail to provide accurate, real-time performance analysis under varying operating conditions. This paper addresses these challenges by proposing a Long Short-Term Memory (LSTM) based deep learning model designed for systematic performance analysis of PV systems. The model, achieving a high accuracy of 90.89%, offers an advanced solution for predictive maintenance and fault detection. Data were collected from a 200W PV system installed at an educational institution in Madurai, using real-time electrical parameters to train and validate the model in MATLAB/SIMULINK. By integrating renewable energy engineering with artificial intelligence and real-time data analytics, this interdisciplinary approach offers a novel framework to enhance the reliability and sustainability of solar energy systems, contributing to the global effort to overcome key challenges in the renewable energy sector.