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Agent-based appliance scheduling for energy management in industry 4.0

Petri, Ioan ORCID:, Yama, Aida and Rezgui, Yacine ORCID: 2019. Agent-based appliance scheduling for energy management in industry 4.0. Presented at: GECON 2019: International Conference on the Economics of Grids, Clouds, Systems, and Services, Leeds, UK, 17-19 Sep 2019. Published in: Djemame, Karim, Altmann, Jörn, Bañares, José Ángel, Ben-Yehuda, Orna Agmon and Naldi, Maurizio eds. Economics of Grids, Clouds, Systems, and Services. Lecture Notes in Artificial Intelligence. Lecture Notes in Computer Science , vol.11819 Cham, Switzerland: Springer Verlag, pp. 199-207. 10.1007/978-3-030-36027-6_17

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With the growing concerns regarding energy consumption, companies and industries worldwide are looking for ways to reduce their costs and carbon footprint linked to energy usage. The rising cost of energy makes energy saving and optimisation a real stake for businesses which have started to implement more intelligent energy management techniques to achieve a reduction of costs. As industries migrate towards more renewable energy sources and more sustainable consumption models, decentralised energy infrastructure is required where actors can manage and monetise energy capabilities. In fish processing industries, energy is utilised to operate a range of cold rooms and freer units to store and process fish. Modelling thermal loads, appliance scheduling and integration of renewable energy represent key aspects in such industries. To enable the transition towards Industry 4.0 and to efficiently optimise energy in fish industries, multi-agent systems can provide the mechanisms for managing energy consumption and production with standalone entities that can interact and exchange energy with a view of achieving more flexible and informed energy use. In this paper, we propose a multi-agent coordination framework for managing energy in the fish processing industry. We demonstrate how agents can be devised to model appliances and buildings and to support the formation of smart energy clusters. We validate our research based on a real use-case scenario in Milford Haven port in South Wales by showing how multi-agent systems can be implemented and tested for a real fish industrial site.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Springer Verlag
ISBN: 978-3-030-36026-9
ISSN: 0302-9743
Last Modified: 26 Oct 2022 08:19

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