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

页码：242 / 255

页数：14

共 61 条

[21] Basic actions to improve energy efficiency in commercial buildings in operation [J].

Escriva-Escriva, Guillermo .

ENERGY AND BUILDINGS, 2011, 43 (11) :3106-3111

[22]

Fenz S., 2014, COMPUT SCI RES DEV, P1

[23] A SCADA system for energy management in intelligent buildings [J].

Figueiredo, Joao ;

da Costa, Jose Sa .

ENERGY AND BUILDINGS, 2012, 49 :85-98

[24] The essence of future smart houses: From embedding ICT to adapting to sustainability principles [J].

GhaffarianHoseini, AmirHosein ;

Dahlan, Nur Dalilah ;

Berardi, Umberto ;

GhaffarianHoseini, Ali ;

Makaremi, Nastaran .

RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2013, 24 :593-607

[25] Understanding the energy consumption and occupancy of a multi-purpose academic building [J].

Gul, Mehreen S. ;

Patidar, Sandhya .

ENERGY AND BUILDINGS, 2015, 87 :155-165

[26] Exploring new techniques for displaying complex building energy consumption data [J].

Haberl, J ;

Sparks, R ;

Culp, C .

ENERGY AND BUILDINGS, 1996, 24 (01) :27-38

[27]

Han J., 2012, Data Mining, P393, DOI [DOI 10.1016/B978-0-12-381479-1.00009-5, 10.1016/B978-0-12-381479-1.00009-5]

[28] Data mining based sensor fault diagnosis and validation for building air conditioning system [J].

Hou, Zhijian ;

Lian, Zhiwei ;

Yao, Ye ;

Yuan, Xinjian .

ENERGY CONVERSION AND MANAGEMENT, 2006, 47 (15-16) :2479-2490

[29]

Hurtado L., 2014, Proc. IEEE Innovative Smart Grid Technologies Conference Europe, P1, DOI [10.1109/ISGTEurope.2014.7028937, DOI 10.1109/ISGTEUROPE.2014.7028937]

[30]

Jing Wang, 2012, 2012 IEEE International Conference on Information Science and Technology, P124, DOI 10.1109/ICIST.2012.6221620

← 1 2 3 4 5 6 7 →