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Rule-based system to detect energy efficiency anomalies in smart buildings, a data mining approach

 

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Opened Access Rule-based system to detect energy efficiency anomalies in smart buildings, a data mining approach
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Author: Peña, Manuel
Biscarri Triviño, Félix
Guerrero Alonso, Juan Ignacio
Monedero Goicoechea, Iñigo Luis
León de Mora, Carlos
Department: Universidad de Sevilla. Departamento de Tecnología Electrónica
Date: 2016
Published in: Expert Systems with Applications, 56 (september 2016), 242-255.
Document type: Article
Abstract: The rapidly growing world energy use already has concerns over the exhaustion of energy resources andheavy environmental impacts. As a result of these concerns, a trend of green and smart cities has beenincreasing. To respond to this increasing trend of smart cities with buildings every time more complex,in this paper we have proposed a new method to solve energy inefficiencies detection problem in smartbuildings. This solution is based on a rule-based system developed through data mining techniques andapplying the knowledge of energy efficiency experts. A set of useful energy efficiency indicators is alsoproposed to detect anomalies. The data mining system is developed through the knowledge extracted bya full set of building sensors. So, the results of this process provide a set of rules that are used as a partof a decision support system for the optimisation of energy consumption and the detection of anomaliesin smart buildings.
Cite: Peña, M., Biscarri Triviño, F., Guerrero Alonso, J.I., Monedero Goicoechea, I.L. y León de Mora, C. (2016). Rule-based system to detect energy efficiency anomalies in smart buildings, a data mining approach. Expert Systems with Applications, 56 (september 2016), 242-255.
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Format: PDF

URI: https://hdl.handle.net/11441/76896

DOI: 10.1016/j.eswa.2016.03.002

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