A perspective on the enabling technologies of explainable AI-based industrial packetized energy management

Daniel Gutierrez-Rojas*, Arun Narayanan, Cássia R. Santos Nunes Almeida, Gustavo M. Almeida, Diana Pfau, Yu Tian, Xu Yang, Alex Jung, Pedro H.J. Nardelli

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
58 Downloads (Pure)

Abstract

This paper reviews the key information and communication technologies that are necessary to build an effective industrial energy management system considering the intermittence of renewable sources like wind and solar . In particular, we first introduce the concept of software-defined energy networks in the context of industrial cyber-physical systems aiming at the optimal energy resource allocation in terms of its environmental impact. The task is formulated as a dynamic scheduling problem where supply and demand must match at minute-level timescale, also considering energy storage units. The use of (explainable and trustworthy) artificial intelligence (AI), (informative) networked data, demand-side management, machine-type (wireless) communications, and energy-aware scheduling in industrial plants are explored in detail. The paper also provides a framework for understanding the complexities of managing renewable energy sources in industrial plants while maintaining efficiency and environmental sustainability.

Original languageEnglish
Article number108415
JournaliScience
Volume26
Issue number12
DOIs
Publication statusPublished - 15 Dec 2023
MoE publication typeA1 Journal article-refereed

Keywords

  • Energy management
  • Energy Modeling
  • Energy resources

Fingerprint

Dive into the research topics of 'A perspective on the enabling technologies of explainable AI-based industrial packetized energy management'. Together they form a unique fingerprint.

Cite this