Prediction of government-owned building energy consumption based on an RReliefF and support vector machine model
- Authors
- Son, Hyojoo; Kim, Changmin; Kim, Changwan; Kang, Youngcheol
- Issue Date
- Aug-2015
- Publisher
- VILNIUS GEDIMINAS TECH UNIV
- Keywords
- sustainable development; support vector machine model; energy consumption prediction; government-owned building; RReliefF variable selection
- Citation
- JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, v.21, no.6, pp 748 - 760
- Pages
- 13
- Journal Title
- JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT
- Volume
- 21
- Number
- 6
- Start Page
- 748
- End Page
- 760
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/9223
- DOI
- 10.3846/13923730.2014.893908
- ISSN
- 1392-3730
1822-3605
- 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.
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