PREDICTION OF GOVERNMENT-OWNED BUILDING ENERGY CONSUMPTION BASED ON AN RRELIEFF AND SUPPORT VECTOR MACHINE MODEL

Journal of Civil Engineering and Management - Tập 21 Số 6 - Trang 748-760
Hyojoo Son1, Changmin Kim1, Changwan Kim1, Youngcheol Kang2
1Department of Architectural Engineering, Chung-Ang University, 156-756 Seoul, Korea
2Department of Global Construction Management, The University of Seoul, Liberal Arts Building 5224, 163 Siripdaero, Dongdaemun-gu, 130-743 Seoul, Korea

Tóm tắt

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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Thông tin xuất bản

Journal of Civil Engineering and Management

Tập 21 Số 6

748-760

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