Big data : the key to energy efficiency in smart buildings

Moreno, Victoria (Department of Information and Communications Engineering, University of Murcia, Murcia, Spain) ; Dufour, Luc (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; Skarmeta, Antonio (Department of Information and Communications Engineering, University of Murcia, Murcia, Spain) ; Jara, Antonio (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; Genoud, Dominique (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; Ladevie, Bruno (Mines Telecom, Albi, France) ; Bezian, JeanJacques (Mines Telecom, Albi, France)

Due to the high impact that energy consumption of buildings has at global scale, it has been stated the need of achieving energy-efficient buildings to reduce CO2 emissions and energy consumption at global scale. In this work we present a novel approach to generate models of the building energy consumption associated with comfort services. Thus, analysing these models enables select the best strategies to save energy. To verify the feasibility of this proposal, we carry out some analysis in a reference building of which we have contextual data. Firstly, we provide a complete characterization of this building in term of its energy consumption and generate accurate building models able to predict its energy consumption given a concrete set of inputs. Finally, considering the generated energy usage proile of the building, we propose some concrete control actions and strategies to save energy.


Mots-clés:
Type d'article:
scientifique
Faculté:
Economie et Services
Ecole:
HEG-VS
Institut:
Institut Informatique de gestion
Classification:
Informatique
Date:
2015
Publié dans
Soft computing manuscrit
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Note: The status of this file is: restricted


 Notice créée le 2015-11-13, modifiée le 2018-12-11

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