Machine learning for energy management

Richard Bean, Stephen Snow, Xiang Li, Mashhuda Glencross and Archie C. Chapman

Chapter 6 in Research Handbook on Energy Management, 2025, pp 123-159 from Edward Elgar Publishing

Abstract: This chapter reviews the use of machine-learning methods in the management, operation, and planning of energy systems. It highlights the great potential for machine learning to reduce energy system costs and to provide greater value back to energy users from existing energy infrastructure. Four specific application domains are reviewed: forecasting for energy scheduling, with an emphasis on macro- versus micro-level forecasting and decision-making; methods for typical load profile generation; non-intrusive load monitoring and associated techniques for customer-level energy analytics; and machine-learning-informed visualization in energy systems. We highlight important use cases of machine learning in energy management and identify opportunities and challenges to implementing machine-learning-based solutions.

Keywords: Energy management; Machine learning; Forecasting; Load profiles; Non-intrusive load monitoring; Visualization (search for similar items in EconPapers)
Date: 2025
ISBN: 9781800376496
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