Energy Optimization
Anshuka Bansal, Ashwani Kumar Aggarwal, Anita Khosla · 2025
This chapter explores energy optimization, a key element in meeting global demands for sustainable energy systems. It identifies challenges such as integrating renewable sources into existing grids, managing their variability, and balancing supply and demand while minimizing losses. The chapter emphasizes the role of machine learning (ML) in addressing these challenges and reviews various supervised, unsupervised, reinforcement ML techniques, and deep learning and their applications in predictive maintenance, load forecasting, and energy distribution optimization. It also covers technical considerations, including data management and model integration, and highlights the need for interdisciplinary collaboration. Emerging trends like IoT, edge computing, and advanced sensors are discussed as facilitators of improved energy management. The chapter advocates for ongoing research and innovation in ML to advance efficient, resilient, and sustainable energy systems.