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

被引：71
作者：
Pena, Manuel [1 ]
Biscarri, Felix [1 ]
Ignacio Guerrero, Juan [1 ]
Monedero, Inigo [1 ]
Leon, Carlos [1 ]
机构：
[1] Univ Seville, Escuela Politecn Super, Dept Tecnol Elect, C Virgen Africa 7, Seville 41011, Spain
来源：
EXPERT SYSTEMS WITH APPLICATIONS | 2016年 / 56卷
关键词：
Energy efficiency; Smart building; Energy efficiency indicators; Analytics; Expert System; Decision support system; COMFORT MANAGEMENT; RENEWABLE ENERGY; MULTIOBJECTIVE OPTIMIZATION; CONSUMPTION; AUTOMATION; GREENHOUSE; METHODOLOGY; ENVIRONMENT; POLICY; MODEL;
D O I：
10.1016/j.eswa.2016.03.002
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The rapidly growing world energy use already has concerns over the exhaustion of energy resources and heavy environmental impacts. As a result of these concerns, a trend of green and smart cities has been increasing. 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 smart buildings. This solution is based on a rule-based system developed through data mining techniques and applying the knowledge of energy efficiency experts. A set of useful energy efficiency indicators is also proposed to detect anomalies. The data mining system is developed through the knowledge extracted by a full set of building sensors. So, the results of this process provide a set of rules that are used as a part of a decision support system for the optimisation of energy consumption and the detection of anomalies in smart buildings. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码：242 / 255
页数：14
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