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Sensors (Basel). 2018 May 19;18(5). pii: E1633. doi: 10.3390/s18051633.

Agreement Technologies for Energy Optimization at Home.

Author information

1
BISITE Digital Innovation Hub, University of Salamanca, Edificio Multiusos I+D+i, 37007 Salamanca, Spain. alfonsogb@usal.es.
2
BISITE Digital Innovation Hub, University of Salamanca, Edificio Multiusos I+D+i, 37007 Salamanca, Spain. chamoso@usal.es.
3
BISITE Digital Innovation Hub, University of Salamanca, Edificio Multiusos I+D+i, 37007 Salamanca, Spain. fer@usal.es.
4
Centre National de le Recherche Scientifique - Laboratoire d'Informatique de Grenoble (CNRS-LIG), University of Grenoble-Alps, 38000 Grenoble, France. yves.demazeau@imag.fr.
5
BISITE Digital Innovation Hub, University of Salamanca, Edificio Multiusos I+D+i, 37007 Salamanca, Spain. corchado@usal.es.
6
Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, 535-8585 Osaka, Japan. corchado@usal.es.
7
Pusat Komputeran dan Informatik, Universiti Malaysia Kelantan, Karung Berkunci 36, Pengkaan Chepa, 16100 Kota Bharu, Kelantan, Malaysia. corchado@usal.es.

Abstract

Nowadays, it is becoming increasingly common to deploy sensors in public buildings or homes with the aim of obtaining data from the environment and taking decisions that help to save energy. Many of the current state-of-the-art systems make decisions considering solely the environmental factors that cause the consumption of energy. These systems are successful at optimizing energy consumption; however, they do not adapt to the preferences of users and their comfort. Any system that is to be used by end-users should consider factors that affect their wellbeing. Thus, this article proposes an energy-saving system, which apart from considering the environmental conditions also adapts to the preferences of inhabitants. The architecture is based on a Multi-Agent System (MAS), its agents use Agreement Technologies (AT) to perform a negotiation process between the comfort preferences of the users and the degree of optimization that the system can achieve according to these preferences. A case study was conducted in an office building, showing that the proposed system achieved average energy savings of 17.15%.

KEYWORDS:

agreement technologies; building automation; energy saving; multi-agent systems; negotiation

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