A decision support model for improving a multi-family housing complex based on CO2 emission from electricity consumption

Taehoon Hong, Choongwan Koo, Hyunjoong Kim

Research output: Contribution to journalArticle

38 Citations (Scopus)

Abstract

The number of deteriorated multi-family housing complexes in South Korea continues to rise, and consequently their electricity consumption is also increasing. This needs to be addressed as part of the nation's efforts to reduce energy consumption. The objective of this research was to develop a decision support model for determining the need to improve multi-family housing complexes. In this research, 1664 cases located in Seoul were selected for model development. The research team collected the characteristics and electricity energy consumption data of these projects in 2009-2010. The following were carried out in this research: (i) using the Decision Tree, multi-family housing complexes were clustered based on their electricity energy consumption; (ii) using Case-Based Reasoning, similar cases were retrieved from the same cluster; and (iii) using a combination of Multiple Regression Analysis, Artificial Neural Network, and Genetic Algorithm, the prediction performance of the developed model was improved. The results of this research can be used as follows: (i) as basic research data for continuously managing several energy consumption data of multi-family housing complexes; (ii) as advanced research data for predicting energy consumption based on the project characteristics; (iii) as practical research data for selecting the most optimal multi-family housing complex with the most potential in terms of energy savings; and (iv) as consistent and objective criteria for incentives and penalties.

Original languageEnglish
Pages (from-to)67-78
Number of pages12
JournalJournal of Environmental Management
Volume112
DOIs
Publication statusPublished - 2012 Dec 15

Fingerprint

Electricity
Energy utilization
electricity
Case based reasoning
family
electricity consumption
decision
Decision trees
Regression analysis
genetic algorithm
artificial neural network
multiple regression
incentive
Energy conservation
regression analysis
Genetic algorithms
energy consumption
Neural networks
prediction

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Waste Management and Disposal
  • Management, Monitoring, Policy and Law

Cite this

@article{7b92acbf3fbf481ca0c5f6dc17021bb7,
title = "A decision support model for improving a multi-family housing complex based on CO2 emission from electricity consumption",
abstract = "The number of deteriorated multi-family housing complexes in South Korea continues to rise, and consequently their electricity consumption is also increasing. This needs to be addressed as part of the nation's efforts to reduce energy consumption. The objective of this research was to develop a decision support model for determining the need to improve multi-family housing complexes. In this research, 1664 cases located in Seoul were selected for model development. The research team collected the characteristics and electricity energy consumption data of these projects in 2009-2010. The following were carried out in this research: (i) using the Decision Tree, multi-family housing complexes were clustered based on their electricity energy consumption; (ii) using Case-Based Reasoning, similar cases were retrieved from the same cluster; and (iii) using a combination of Multiple Regression Analysis, Artificial Neural Network, and Genetic Algorithm, the prediction performance of the developed model was improved. The results of this research can be used as follows: (i) as basic research data for continuously managing several energy consumption data of multi-family housing complexes; (ii) as advanced research data for predicting energy consumption based on the project characteristics; (iii) as practical research data for selecting the most optimal multi-family housing complex with the most potential in terms of energy savings; and (iv) as consistent and objective criteria for incentives and penalties.",
author = "Taehoon Hong and Choongwan Koo and Hyunjoong Kim",
year = "2012",
month = "12",
day = "15",
doi = "10.1016/j.jenvman.2012.06.046",
language = "English",
volume = "112",
pages = "67--78",
journal = "Journal of Environmental Management",
issn = "0301-4797",
publisher = "Academic Press Inc.",

}

A decision support model for improving a multi-family housing complex based on CO2 emission from electricity consumption. / Hong, Taehoon; Koo, Choongwan; Kim, Hyunjoong.

In: Journal of Environmental Management, Vol. 112, 15.12.2012, p. 67-78.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A decision support model for improving a multi-family housing complex based on CO2 emission from electricity consumption

AU - Hong, Taehoon

AU - Koo, Choongwan

AU - Kim, Hyunjoong

PY - 2012/12/15

Y1 - 2012/12/15

N2 - The number of deteriorated multi-family housing complexes in South Korea continues to rise, and consequently their electricity consumption is also increasing. This needs to be addressed as part of the nation's efforts to reduce energy consumption. The objective of this research was to develop a decision support model for determining the need to improve multi-family housing complexes. In this research, 1664 cases located in Seoul were selected for model development. The research team collected the characteristics and electricity energy consumption data of these projects in 2009-2010. The following were carried out in this research: (i) using the Decision Tree, multi-family housing complexes were clustered based on their electricity energy consumption; (ii) using Case-Based Reasoning, similar cases were retrieved from the same cluster; and (iii) using a combination of Multiple Regression Analysis, Artificial Neural Network, and Genetic Algorithm, the prediction performance of the developed model was improved. The results of this research can be used as follows: (i) as basic research data for continuously managing several energy consumption data of multi-family housing complexes; (ii) as advanced research data for predicting energy consumption based on the project characteristics; (iii) as practical research data for selecting the most optimal multi-family housing complex with the most potential in terms of energy savings; and (iv) as consistent and objective criteria for incentives and penalties.

AB - The number of deteriorated multi-family housing complexes in South Korea continues to rise, and consequently their electricity consumption is also increasing. This needs to be addressed as part of the nation's efforts to reduce energy consumption. The objective of this research was to develop a decision support model for determining the need to improve multi-family housing complexes. In this research, 1664 cases located in Seoul were selected for model development. The research team collected the characteristics and electricity energy consumption data of these projects in 2009-2010. The following were carried out in this research: (i) using the Decision Tree, multi-family housing complexes were clustered based on their electricity energy consumption; (ii) using Case-Based Reasoning, similar cases were retrieved from the same cluster; and (iii) using a combination of Multiple Regression Analysis, Artificial Neural Network, and Genetic Algorithm, the prediction performance of the developed model was improved. The results of this research can be used as follows: (i) as basic research data for continuously managing several energy consumption data of multi-family housing complexes; (ii) as advanced research data for predicting energy consumption based on the project characteristics; (iii) as practical research data for selecting the most optimal multi-family housing complex with the most potential in terms of energy savings; and (iv) as consistent and objective criteria for incentives and penalties.

UR - http://www.scopus.com/inward/record.url?scp=84864746983&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84864746983&partnerID=8YFLogxK

U2 - 10.1016/j.jenvman.2012.06.046

DO - 10.1016/j.jenvman.2012.06.046

M3 - Article

C2 - 22877743

AN - SCOPUS:84864746983

VL - 112

SP - 67

EP - 78

JO - Journal of Environmental Management

JF - Journal of Environmental Management

SN - 0301-4797

ER -