A decision support model for improving a multi-family housing complex based on CO 2 emission from gas energy consumption

Taehoon Hong, Choongwan Koo, Sungki Park

Research output: Contribution to journalArticle

53 Citations (Scopus)

Abstract

Improvement of residential environments has recently been promoted by the Korean government as part of its energy-saving measures. The objective of this research is to develop a decision support model for selecting the multi-family housing complex with the potential to be effective in saving energy. In this research, 362 cases of multi-family housings located in Seoul were selected to collect characteristics and data on gas energy consumption from 2009 to 2010. The following were carried out: (i) using the Decision Tree, a group of multi-family housings was established based on gas energy consumption; (ii)using case-based reasoning, a number of similar multi-family housings were retrieved from the same group of multi-family housings; and (iii) using a combination of genetic algorithms, artificial neural network, and multiple regression analysis, prediction accuracy was improved. The results of this research can be useful in the following: (i) preliminary research for continuously managing the gas energy consumption of multi-family housings; (ii) basic research for predicting gas energy consumption based on the characteristics of multi-family housings; and (iii) practical research for selecting an optimum multi-family housing complex (with the potential to be effective in saving gas energy), which can make the application of an energy-saving program more effective as a decision support model.

Original languageEnglish
Pages (from-to)142-151
Number of pages10
JournalBuilding and Environment
Volume52
DOIs
Publication statusPublished - 2012 Jun 1

Fingerprint

energy consumption
Energy utilization
housing
Gases
gas
energy saving
Energy conservation
residential environment
basic research
Case based reasoning
family
decision
Decision trees
Regression analysis
genetic algorithm
neural network
artificial neural network
multiple regression
regression analysis
Group

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Civil and Structural Engineering
  • Geography, Planning and Development
  • Building and Construction

Cite this

@article{530a0621299a4dd89e29678b73e7c74f,
title = "A decision support model for improving a multi-family housing complex based on CO 2 emission from gas energy consumption",
abstract = "Improvement of residential environments has recently been promoted by the Korean government as part of its energy-saving measures. The objective of this research is to develop a decision support model for selecting the multi-family housing complex with the potential to be effective in saving energy. In this research, 362 cases of multi-family housings located in Seoul were selected to collect characteristics and data on gas energy consumption from 2009 to 2010. The following were carried out: (i) using the Decision Tree, a group of multi-family housings was established based on gas energy consumption; (ii)using case-based reasoning, a number of similar multi-family housings were retrieved from the same group of multi-family housings; and (iii) using a combination of genetic algorithms, artificial neural network, and multiple regression analysis, prediction accuracy was improved. The results of this research can be useful in the following: (i) preliminary research for continuously managing the gas energy consumption of multi-family housings; (ii) basic research for predicting gas energy consumption based on the characteristics of multi-family housings; and (iii) practical research for selecting an optimum multi-family housing complex (with the potential to be effective in saving gas energy), which can make the application of an energy-saving program more effective as a decision support model.",
author = "Taehoon Hong and Choongwan Koo and Sungki Park",
year = "2012",
month = "6",
day = "1",
doi = "10.1016/j.buildenv.2012.01.001",
language = "English",
volume = "52",
pages = "142--151",
journal = "Building and Environment",
issn = "0360-1323",
publisher = "Elsevier BV",

}

A decision support model for improving a multi-family housing complex based on CO 2 emission from gas energy consumption. / Hong, Taehoon; Koo, Choongwan; Park, Sungki.

In: Building and Environment, Vol. 52, 01.06.2012, p. 142-151.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A decision support model for improving a multi-family housing complex based on CO 2 emission from gas energy consumption

AU - Hong, Taehoon

AU - Koo, Choongwan

AU - Park, Sungki

PY - 2012/6/1

Y1 - 2012/6/1

N2 - Improvement of residential environments has recently been promoted by the Korean government as part of its energy-saving measures. The objective of this research is to develop a decision support model for selecting the multi-family housing complex with the potential to be effective in saving energy. In this research, 362 cases of multi-family housings located in Seoul were selected to collect characteristics and data on gas energy consumption from 2009 to 2010. The following were carried out: (i) using the Decision Tree, a group of multi-family housings was established based on gas energy consumption; (ii)using case-based reasoning, a number of similar multi-family housings were retrieved from the same group of multi-family housings; and (iii) using a combination of genetic algorithms, artificial neural network, and multiple regression analysis, prediction accuracy was improved. The results of this research can be useful in the following: (i) preliminary research for continuously managing the gas energy consumption of multi-family housings; (ii) basic research for predicting gas energy consumption based on the characteristics of multi-family housings; and (iii) practical research for selecting an optimum multi-family housing complex (with the potential to be effective in saving gas energy), which can make the application of an energy-saving program more effective as a decision support model.

AB - Improvement of residential environments has recently been promoted by the Korean government as part of its energy-saving measures. The objective of this research is to develop a decision support model for selecting the multi-family housing complex with the potential to be effective in saving energy. In this research, 362 cases of multi-family housings located in Seoul were selected to collect characteristics and data on gas energy consumption from 2009 to 2010. The following were carried out: (i) using the Decision Tree, a group of multi-family housings was established based on gas energy consumption; (ii)using case-based reasoning, a number of similar multi-family housings were retrieved from the same group of multi-family housings; and (iii) using a combination of genetic algorithms, artificial neural network, and multiple regression analysis, prediction accuracy was improved. The results of this research can be useful in the following: (i) preliminary research for continuously managing the gas energy consumption of multi-family housings; (ii) basic research for predicting gas energy consumption based on the characteristics of multi-family housings; and (iii) practical research for selecting an optimum multi-family housing complex (with the potential to be effective in saving gas energy), which can make the application of an energy-saving program more effective as a decision support model.

UR - http://www.scopus.com/inward/record.url?scp=84862776867&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84862776867&partnerID=8YFLogxK

U2 - 10.1016/j.buildenv.2012.01.001

DO - 10.1016/j.buildenv.2012.01.001

M3 - Article

AN - SCOPUS:84862776867

VL - 52

SP - 142

EP - 151

JO - Building and Environment

JF - Building and Environment

SN - 0360-1323

ER -