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Agent-Based Appliance Scheduling for Energy Management in Industry 4.0

  • Ioan PetriEmail author
  • Aida Yama
  • Yacine Rezgui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11819)

Abstract

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.

Keywords

Multi-agent systems Appliance scheduling Energy management Cost Smart industries 

Notes

Acknowledgments

This work is part of the EU INTERREG piSCES project: “Smart Cluster Energy System for the Fish Processing Industry”, grant number 504460.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of EngineeringCardiff UniversityCardiffUK
  2. 2.IMT Mines Albi-Carnaux, Ecole Mines-TelekomAlbiFrance

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