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.
Bibliographical noteFunding Information:
This research was supported by a grant from High-Tech Urban Development Program (10CHUD-C03) funded by the Ministry of Land, Transport and Maritime affairs , South Korea. This research was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government ( Ministry of Education, Science and Technology , MEST) (No. 2011-0018360 ).
All Science Journal Classification (ASJC) codes
- Environmental Engineering
- Civil and Structural Engineering
- Geography, Planning and Development
- Building and Construction