Prediction of government-owned building energy consumption based on an RReliefF and support vector machine model

Hyojoo Son, Changwan Kim, Changmin Kim, Youngcheol Kang

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Accurate prediction of the energy consumption of government-owned buildings in the design phase is vital for government agencies, as it enables formulation of the early phases of development of such buildings with a view to reducing their environmental impact. The aim of this study was to identify the variables that are associated with energy consumption in government-owned buildings and to propose a predictive model based on those variables. The proposed approach selects relevant variables using the RReliefF variable selection algorithm. The support vector machine (SVM) method is used to develop a model of energy consumption based on the identified variables. The proposed approach was analyzed and validated on data for 175 government-owned buildings derived from the 2003 Commercial Building Energy Consumption Survey (CBECS) database. The experimental results revealed that the proposed model is able to predict the energy consumption of government-owned buildings in the design phase with a reasonable level of accuracy. The proposed model could be beneficial in guiding government agencies in developing early strategies and proactively reducing the environmental impact of a building, thereby achieving a high degree of sustainability of buildings constructed for government agencies.

Original languageEnglish
Pages (from-to)748-760
Number of pages13
JournalJournal of Civil Engineering and Management
Volume21
Issue number6
DOIs
Publication statusPublished - 2015 Aug 18

Fingerprint

Support vector machines
Energy utilization
Environmental impact
Support vector machine
Energy consumption
Prediction
Government
Sustainable development
Government agencies

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Strategy and Management

Cite this

@article{ed79027945e94c209ff7f2e2b91203d1,
title = "Prediction of government-owned building energy consumption based on an RReliefF and support vector machine model",
abstract = "Accurate prediction of the energy consumption of government-owned buildings in the design phase is vital for government agencies, as it enables formulation of the early phases of development of such buildings with a view to reducing their environmental impact. The aim of this study was to identify the variables that are associated with energy consumption in government-owned buildings and to propose a predictive model based on those variables. The proposed approach selects relevant variables using the RReliefF variable selection algorithm. The support vector machine (SVM) method is used to develop a model of energy consumption based on the identified variables. The proposed approach was analyzed and validated on data for 175 government-owned buildings derived from the 2003 Commercial Building Energy Consumption Survey (CBECS) database. The experimental results revealed that the proposed model is able to predict the energy consumption of government-owned buildings in the design phase with a reasonable level of accuracy. The proposed model could be beneficial in guiding government agencies in developing early strategies and proactively reducing the environmental impact of a building, thereby achieving a high degree of sustainability of buildings constructed for government agencies.",
author = "Hyojoo Son and Changwan Kim and Changmin Kim and Youngcheol Kang",
year = "2015",
month = "8",
day = "18",
doi = "10.3846/13923730.2014.893908",
language = "English",
volume = "21",
pages = "748--760",
journal = "Journal of Civil Engineering and Management",
issn = "1392-3730",
publisher = "Vilnius Gediminas Technical University",
number = "6",

}

Prediction of government-owned building energy consumption based on an RReliefF and support vector machine model. / Son, Hyojoo; Kim, Changwan; Kim, Changmin; Kang, Youngcheol.

In: Journal of Civil Engineering and Management, Vol. 21, No. 6, 18.08.2015, p. 748-760.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Prediction of government-owned building energy consumption based on an RReliefF and support vector machine model

AU - Son, Hyojoo

AU - Kim, Changwan

AU - Kim, Changmin

AU - Kang, Youngcheol

PY - 2015/8/18

Y1 - 2015/8/18

N2 - Accurate prediction of the energy consumption of government-owned buildings in the design phase is vital for government agencies, as it enables formulation of the early phases of development of such buildings with a view to reducing their environmental impact. The aim of this study was to identify the variables that are associated with energy consumption in government-owned buildings and to propose a predictive model based on those variables. The proposed approach selects relevant variables using the RReliefF variable selection algorithm. The support vector machine (SVM) method is used to develop a model of energy consumption based on the identified variables. The proposed approach was analyzed and validated on data for 175 government-owned buildings derived from the 2003 Commercial Building Energy Consumption Survey (CBECS) database. The experimental results revealed that the proposed model is able to predict the energy consumption of government-owned buildings in the design phase with a reasonable level of accuracy. The proposed model could be beneficial in guiding government agencies in developing early strategies and proactively reducing the environmental impact of a building, thereby achieving a high degree of sustainability of buildings constructed for government agencies.

AB - Accurate prediction of the energy consumption of government-owned buildings in the design phase is vital for government agencies, as it enables formulation of the early phases of development of such buildings with a view to reducing their environmental impact. The aim of this study was to identify the variables that are associated with energy consumption in government-owned buildings and to propose a predictive model based on those variables. The proposed approach selects relevant variables using the RReliefF variable selection algorithm. The support vector machine (SVM) method is used to develop a model of energy consumption based on the identified variables. The proposed approach was analyzed and validated on data for 175 government-owned buildings derived from the 2003 Commercial Building Energy Consumption Survey (CBECS) database. The experimental results revealed that the proposed model is able to predict the energy consumption of government-owned buildings in the design phase with a reasonable level of accuracy. The proposed model could be beneficial in guiding government agencies in developing early strategies and proactively reducing the environmental impact of a building, thereby achieving a high degree of sustainability of buildings constructed for government agencies.

UR - http://www.scopus.com/inward/record.url?scp=84930813634&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84930813634&partnerID=8YFLogxK

U2 - 10.3846/13923730.2014.893908

DO - 10.3846/13923730.2014.893908

M3 - Article

AN - SCOPUS:84930813634

VL - 21

SP - 748

EP - 760

JO - Journal of Civil Engineering and Management

JF - Journal of Civil Engineering and Management

SN - 1392-3730

IS - 6

ER -