Smart Intelligence Perspective and Integrating AI with the Power System

Title

Smart Intelligence Perspective and Integrating AI with the Power System

Creator

Chopra, Gaurika; Jacob, Lija

Description

The confluence of Artificial Intelligence (AI) and energy frameworks has become a focal point in contemporary research, driven by the imperative to innovate and advance our energy systems. The book chapter delves into the nuanced relationship between AI and these systems, highlighting its opportunities for improved performance, resilience, and environmental conservation. Central to this exploration is the elucidation of AIs transformative potential in energy dynamics, spanning areas such as machine learning, deep learning, neural architectures, and foresight modelling. The chapter portrays diverse AI-driven applications, emphasising their transformative capabilities in steering energy strategies. Insights into the multifaceted advantages of integrating AI into energy frameworks are presented, stressing augmented stability, amplified efficiency, fiscal savings, and forward-thinking outage resolutions. Based on real-world examples, the research highlights the value of integrating AI into energy strategies. It also identifies new paradigms in the evolving energy landscape that will shape our energy future. This underscores AIs crucial role in driving energy transformations and calls for collaboration among academia, decision-makers, and industry leaders to create a greener, more efficient energy path. The importance of data analytics in managing power system data, enabling insights into consumption trends, and assisting in making educated decisions is also covered in this chapter. In order to ensure a responsible and secure implementation, the ethical and privacy issues associated with AI deployment in the power sector are also addressed. Furthermore, the chapter elaborates on the prospective trajectory of AI within Power Systems, elucidating its involvement in quantum computing, edge computing, and the incorporation of IoT to facilitate Microgrid Management. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Source

Power Systems;Volume;Part F914;pp.1-17

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

AI-driven applications; Artificial Intelligence (AI); Contemporary research; Edge computing; Energy frameworks; Environmental conservation; Foresight modelling; Microgrid Management; Quantum computing

Coverage

Chopra G., Christ University, School of Sciences, Pune, Lavasa, India; Jacob L., Christ University, School of Sciences, Pune, Lavasa, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 16121287;

Format

online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1007/978-3-031-89459-6_1

https://www.scopus.com/pages/publications/105018013423?origin=resultslist