Next Article in Journal
3D Target Localization of Modified 3D MUSIC for a Triple-Channel K-Band Radar
Next Article in Special Issue
An Efficient Neural-Network-Based Microseismic Monitoring Platform for Hydraulic Fracture on an Edge Computing Architecture
Previous Article in Journal
Integrated Framework of Load Monitoring by a Combination of Smartphone Applications, Wearables and Point-of-Care Testing Provides Feedback that Allows Individual Responsive Adjustments to Activities of Daily Living
Previous Article in Special Issue
Residual Error Based Anomaly Detection Using Auto-Encoder in SMD Machine Sound
Open AccessArticle

Agreement Technologies for Energy Optimization at Home

BISITE Digital Innovation Hub, University of Salamanca, Edificio Multiusos I+D+i, 37007 Salamanca, Spain
Centre National de le Recherche Scientifique - Laboratoire d’Informatique de Grenoble (CNRS-LIG), University of Grenoble-Alps, 38000 Grenoble, France
Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, 535-8585 Osaka, Japan
Pusat Komputeran dan Informatik, Universiti Malaysia Kelantan, Karung Berkunci 36, Pengkaan Chepa, 16100 Kota Bharu, Kelantan, Malaysia
Authors to whom correspondence should be addressed.
Sensors 2018, 18(5), 1633;
Received: 10 April 2018 / Revised: 14 May 2018 / Accepted: 15 May 2018 / Published: 19 May 2018
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
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%. View Full-Text
Keywords: energy saving; agreement technologies; building automation; negotiation; multi-agent systems energy saving; agreement technologies; building automation; negotiation; multi-agent systems
Show Figures

Figure 1

MDPI and ACS Style

González-Briones, A.; Chamoso, P.; De La Prieta, F.; Demazeau, Y.; Corchado, J.M. Agreement Technologies for Energy Optimization at Home. Sensors 2018, 18, 1633.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop