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Title	Smart Energy Management: From Conventional Optimization To Generative Ai Techniques
ID_Doc	50838
Authors	Mongaillard T.; Lasaulce S.; Varma V.S.
Year	2025
Published	Lecture Notes in Intelligent Transportation and Infrastructure, Part F99
DOI	http://dx.doi.org/10.1007/978-3-031-72959-1_15
Abstract	This chapter explores the evolution of power consumption scheduling in smart cities, focusing on smart homes and electric vehicle charging. It discusses the transition from classical optimization techniques to heuristic methods like genetic algorithms and swarm optimization for addressing complex energy management problems. The chapter also highlights the growing importance of machine learning, particularly artificial neural networks and reinforcement learning, in predicting energy demand and optimizing scheduling decisions. Additionally, it delves into the potential of generative AI, including generative adversarial networks and large language models, to revolutionize power scheduling by generating realistic scenarios, improving user interaction, and enabling more personalized and efficient energy management strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
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