Energy Optimization Using a Case-Based Reasoning Strategy
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AuthorGonzález-Briones, Alfonso; Prieto, Javier; De La Prieta, Fernando; Herrera Viedma, Enrique; Corchado, Juan M.
Smart buildingUbiquitous computingIntelligent managementCase-based reasoning
González-Briones, A. [et al.]. Optimization Using a Case-Based Reasoning Strategy. Sensors 2018, 18, 865. [doi: 10.3390/s18030865]
SponsorshipThis research has been partially supported by the European Regional Development Fund (FEDER) within the framework of the Interreg program V-A Spain-Portugal 2014–2020 (PocTep) under the IOTEC project grant 0123_IOTEC_3_E and by the Spanish Ministry of Economy, Industry and Competitiveness and the European Social Fund under the ECOCASA project grant RTC-2016-5250-6. The research of Alfonso González-Briones has been co-financed by the European Social Fund (Operational Programme 2014–2020 for Castilla y León, EDU/310/2015 BOCYL).
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.