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

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Journal Article

Gutierrez-Rojas, Daniel, Arun Narayanan, Cássia R. Santos Nunes Almeida, Gustavo M. Almeida, Diana Pfau, Yu Tian, Xu Yang, Alex Jung, and Pedro H. J. Nardelli. 2023. “A Perspective on the Enabling Technologies of Explainable AI-Based Industrial Packetized Energy Management.” IScience 26 (12): 108415.

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.

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