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- Title
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
- 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.
- Subjects
ENERGY consumption of public buildings; PREDICTION models; SUPPORT vector machines; ENVIRONMENTAL impact analysis; COMMERCIAL building energy consumption; SUSTAINABLE development
- Publication
Journal of Civil Engineering & Management, 2015, Vol 21, Issue 6, p748
- ISSN
1392-3730
- Publication type
Article
- DOI
10.3846/13923730.2014.893908