An Integrated AI-Multiple Criteria Decision-Making Framework to Improve Sustainable Energy Planning in Manufacturing Systems: A Case Study

Aida Esmaeilidouki (), Bryn J. Crawford (), Amir Ardestani-Jaafari () and Abbas S. Milani ()
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Aida Esmaeilidouki: University of British Columbia
Bryn J. Crawford: University of British Columbia
Amir Ardestani-Jaafari: University of British Columbia
Abbas S. Milani: University of British Columbia

A chapter in Handbook of Smart Energy Systems, 2023, pp 2453-2471 from Springer

Abstract: Abstract Energy planning has historically been a challenging task in sustainable development due to the involvement of multiple criteria, such as social, economic, and environmental impacts (EIs). Multiple criteria decision-making (MCDM) methods have, therefore, attracted much attention to address this challenge. While there have been several opportunities to apply artificial intelligence (AI) and machine learning (ML) algorithms to enable the model to deal with the new situations in solving real-world problems, these methods have not yet been significantly explored in the area of sustainable energy planning. This article develops an insight into the integration of AI with simulation, MCDM technique, and life cycle assessment (LCA) in sustainable energy planning and prospects in this area. An extensive review in this has been performed, and a manufacturing system case study has been developed to illustrate the application of the hybrid proposed framework to improve sustainable energy planning.

Keywords: Energy planning; MCDM; Machine learning; Simulation; LCA (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-97940-9_17

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DOI: 10.1007/978-3-030-97940-9_17

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