Machine Learning in Energy: Enhancing Efficiency, Sustainability, and Grid Management

In response to the evolving global energy landscape, the integration of machine learning techniques has emerged as a promising approach to bolster energy efficiency, foster sustainability, and optimize grid management. This study delves into the utilization of machine learning algorithms and models across various facets of the energy industry, encompassing power generation, distribution, and consumption. By investigating the potential advantages and challenges associated with deploying machine learning technologies in energy systems, the research sheds light on the pivotal domains where machine learning can wield significant influence. Furthermore, the paper explores the significance of data quality, privacy, and security within the realm of machine learning in the energy sector. Ultimately, this research endeavors to offer valuable insights into the present state of machine learning in energy, its potential future advancements, and the implications for the broader energy domain.[i] [i] Mir Sayed Shah Danish, Tomonobu Senjyu : Shaping the future of sustainable energy through AI-enabled circular economy policies. (June 2023)