AI Innovations in Photovoltaic Power Prediction

Abstract

Accurate prediction of solar energy production is essential for integrating photovoltaic (PV) systems into the global energy grid, ensuring grid stability, and optimizing energy management. Traditional PV forecasting methods often face challenges in accounting for the inherent variability in solar radiation and atmospheric conditions. This paper proposes an innovative approach using machine learning (ML) and deep learning (DL) techniques, particularly Long Short-Term Memory (LSTM) networks, to improve short-term PV energy forecasts. Unlike previous models, our work introduces a new combination of input variables, including real-time meteorological data and optimized preprocessing techniques, leading to improved prediction accuracy under varying environmental conditions. The study also demonstrates the importance of high-quality data acquisition, advanced feature selection, and rigorous model training processes for achieving reliable forecasting results. By evaluating multiple models across different scenarios, this research contributes a novel methodology for enhancing the precision of PV power prediction, making it highly suitable for real-world applications in renewable energy integration.