Applications of Artificial Intelligence for Optimizing Sustainable Energy Systems

Abstract

The drive towards sustainable energy systems represents a pivotal global endeavor to address environmental concerns, optimize resource utilization, and ensure a reliable energy supply. In this context, Artificial Intelligence (AI) has emerged as a transformative catalyst, revolutionizing the energy sector's landscape. This study explores the critical role played by AI in the implementation of sustainable energy systems using artificial neural networks (ANNs) to optimize the performance of these systems. ANNs are powerful machine learning algorithms that can be used to model and predict the behavior of complex systems, including sustainable energy systems. By seamlessly integrating AI technologies with renewable energy sources and optimizing energy consumption, AI contributes significantly to enhancing energy system efficiency, reducing carbon footprints, and fostering environmental stewardship. This paper delves into the multifaceted applications of AI in energy, spanning smart grid management, demand forecasting, energy optimization, and grid stability enhancement. The result shows that the hourly predictions of solar energy in Ibadan, Nigeria, have the following errors at 24 hours: MAE (KJ/m2) = 806.35, MSE (KJ/m2) = 159767.44, RMSE (KJ/m2) = 1157.97, Max Error (KJ/m2) = 4638.76, and R-squared (%) = 94.23.