Smart Intelligence Aided Power and Energy Management
Artificial intelligence; Energy management; Grid modernization; Micro grids demand response; Renewable energy integration; Smart grids; Transparency
The Artificial Intelligence (AI) has become a revolutionary technology in power and energy management, providing exceptional prospects for improving efficiency, reliability, and sustainability. This study delves into the incorporation of AI methodologies into smart intelligence-driven systems for power and energy management. It delves into how AI algorithms, encompassing machine learning and optimization approaches, are utilized to enhance energy generation, distribution, and consumption across a range of environments, including smart grids, microgrids, and intelligent buildings. The abstract examines the primary challenges and factors to consider when implementing AI-driven solutions for power and energy management, which encompass issues such as data quality, privacy, security, and scalability. It emphasizes the crucial role of transparency and interpretability in AI algorithms to cultivate trust among stakeholders and secure user acceptance. Additionally, it addresses the importance of upholding ethical standards and regulatory requirements to address societal apprehensions and mitigate potential risks linked to the deployment of AI in energy systems. Moreover, the abstract highlights AIs contribution to advancing energy efficiency and sustainability through dynamic demand response, incorporating renewable resources, and the optimization of grid operations. It underscores the importance of on-going monitoring and evaluation of AI-driven energy management systems to pinpoint areas for enhancement and mitigate unintended repercussions. In summary, this paper offers perspectives on AIs potential to transform power and energy management methodologies, leading to more intelligent, robust, and eco-friendly energy systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Vijayalakshmi, S.; Dahiya, Savita; Gayathri, S.P.
Power Systems;Volume;Part F914;pp.185-196
Springer Science and Business Media Deutschland GmbH
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-3-031-89459-6_10 https://www.scopus.com/pages/publications/105018113429?origin=resultslist
Vijayalakshmi S., Department of Data Science, CHRIST University, Maharashtra, Pune, India; Dahiya S., School of Engineering and Technology, Maharishi University of Information Technology, Uttar Pradesh, Noida, India; Gayathri S.P., Department of Computer Science, Government Arts College for Women, Tamil Nadu, Dindigul, India