The chapter explores the transformative role of artificial intelligence (AI) and machine learning (ML) in shaping smart energy management systems (SEMS) and predicts innovations by 2030. It discusses AI principles in energy optimization, predictive analytics in smart grids, and renewable energy integration through AI-driven strategies. The chapter also addresses critical aspects like predictive maintenance, consumer-centric solutions, cybersecurity challenges, ethical considerations, and regulatory frameworks for responsible AI implementation. By examining challenges and prospects, it provides insights into the dynamic future of energy management driven by AI and ML advancements.
TopIntroduction
Technological advancements and a shift towards sustainable practices have significantly transformed energy management systems. The shift from conventional methods to sophisticated, data-driven systems have reshaped energy production, distribution, and consumption. The energy sector is at the forefront of this transformation, with the integration of Artificial Intelligence (AI) paving the way for a new era of efficiency, optimization, and sustainability in energy management. The integration of AI into energy management systems has revolutionized the industry by processing vast datasets, enabling predictive analysis, and adaptive decision-making. AI algorithms, powered by Machine Learning models, extract valuable insights from energy grids, consumption patterns, weather fluctuations, and infrastructure conditions. This data enables proactive strategies, real-time optimization, and agile responses to changing energy demands (Ganesh & Xu, 2022a).
AI is revolutionizing energy management by transforming the core architecture of energy systems into intelligent, self-learning entities capable of foreseeing, adapting, and optimizing with precision. This includes predictive maintenance, demand forecasting, and grid optimization. AI also plays a crucial role in the seamless integration and harnessing of renewable energy sources, offering sustainable solutions to meet growing energy needs (Satav, Lamani, K. G., et al., 2024; Venkateswaran et al., 2023). This chapter explores the multifaceted realm of AI-driven energy management, navigating its applications, challenges, and ethical considerations. It envisions a future where AI not only optimizes energy but also paves the way towards a sustainable, interconnected, and resilient energy ecosystem. The journey of AI in energy management is not just about innovation but also a testament to the synergy between human ingenuity and technological advancement (Z. Liu et al., 2022a).
Energy management in the past was primarily manual and centralized, with limited adaptability to dynamic demands. However, sustainability and technological advancements have led to a paradigm shift with the emergence of smart grids. These grids use digital technologies for monitoring, control, and optimization of energy flow, with the true revolution emerging from the integration of Artificial Intelligence and Machine Learning (T. Liu, 2022).
AI and ML technologies are revolutionizing energy management by handling vast amounts of data, extracting actionable insights, and enabling predictive and proactive decision-making. AI algorithms, powered by ML models, analyze diverse datasets, including energy consumption patterns and weather forecasts, enabling real-time optimization and adaptive energy systems. AI-empowered systems have revolutionized energy management, from predictive maintenance to demand forecasting (Rashid et al., 2019). It also plays a pivotal role in integrating renewable energy sources into the grid, optimizing their utilization, and balancing supply and demand dynamics. This chapter explores the evolutionary journey of energy management systems, examining applications, challenges, and future possibilities of AI-enabled energy management, envisioning a sustainable and efficient energy future (Kumar B et al., 2024; Rahamathunnisa et al., 2023).
Gap Identified
Despite significant strides in integrating AI into energy management, critical gaps persist. One major challenge lies in data quality and availability, hindering the accuracy of AI algorithms for grid optimization and demand forecasting. Additionally, cybersecurity risks pose a threat to the secure functioning of AI-driven systems, raising concerns about potential attacks on critical energy infrastructure. Ethical considerations surrounding AI decision-making in resource allocation and the lack of standardized regulations further impede widespread adoption. Bridging these gaps necessitates concerted efforts in data infrastructure, cybersecurity protocols, ethical frameworks, and regulatory harmonization to unlock the full potential of AI in revolutionizing energy management.