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Open AccessFeature PaperArticle

Energy Optimization Using a Case-Based Reasoning Strategy

1
BISITE Digital Innovation Hub, University of Salamanca, Edificio I+D+I, 37007 Salamanca, Spain
2
Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain
3
Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan
4
Pusat Komputeran dan Informatik, Universiti Malaysia Kelantan, Karung Berkunci 36, Pengkaan Chepa, Kota Bharu 16100, Kelantan, Malaysia
*
Authors to whom correspondence should be addressed.
Sensors 2018, 18(3), 865; https://doi.org/10.3390/s18030865
Received: 16 January 2018 / Revised: 9 March 2018 / Accepted: 12 March 2018 / Published: 15 March 2018
(This article belongs to the Special Issue Wireless Sensors Networks in Activity Detection and Context Awareness)
At present, the domotization of homes and public buildings is becoming increasingly popular. Domotization is most commonly applied to the field of energy management, since it gives the possibility of managing the consumption of the devices connected to the electric network, the way in which the users interact with these devices, as well as other external factors that influence consumption. In buildings, Heating, Ventilation and Air Conditioning (HVAC) systems have the highest consumption rates. The systems proposed so far have not succeeded in optimizing the energy consumption associated with a HVAC system because they do not monitor all the variables involved in electricity consumption. For this reason, this article presents an agent approach that benefits from the advantages provided by a Multi-Agent architecture (MAS) deployed in a Cloud environment with a wireless sensor network (WSN) in order to achieve energy savings. The agents of the MAS learn social behavior thanks to the collection of data and the use of an artificial neural network (ANN). The proposed system has been assessed in an office building achieving an average energy savings of 41% in the experimental group offices. View Full-Text
Keywords: smart building; ubiquitous computing; intelligent management; case-based reasoning smart building; ubiquitous computing; intelligent management; case-based reasoning
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MDPI and ACS Style

González-Briones, A.; Prieto, J.; De La Prieta, F.; Herrera-Viedma, E.; Corchado, J.M. Energy Optimization Using a Case-Based Reasoning Strategy. Sensors 2018, 18, 865. https://doi.org/10.3390/s18030865

AMA Style

González-Briones A, Prieto J, De La Prieta F, Herrera-Viedma E, Corchado JM. Energy Optimization Using a Case-Based Reasoning Strategy. Sensors. 2018; 18(3):865. https://doi.org/10.3390/s18030865

Chicago/Turabian Style

González-Briones, Alfonso; Prieto, Javier; De La Prieta, Fernando; Herrera-Viedma, Enrique; Corchado, Juan M. 2018. "Energy Optimization Using a Case-Based Reasoning Strategy" Sensors 18, no. 3: 865. https://doi.org/10.3390/s18030865

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