Prediction of government-owned building energy consumption based on an RReliefF and support vector machine model

    Hyojoo Son Info
    Changmin Kim Info
    Changwan Kim Info
    Youngcheol Kang Info

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.

Keywords:

energy consumption prediction, government-owned building, RReliefF variable selection, support vector machine model, sustainable development

How to Cite

Prediction of government-owned building energy consumption based on an RReliefF and support vector machine model. (2015). Journal of Civil Engineering and Management, 21(6), 748-760. https://doi.org/10.3846/13923730.2014.893908

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June 9, 2015
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2015-06-09

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How to Cite

Prediction of government-owned building energy consumption based on an RReliefF and support vector machine model. (2015). Journal of Civil Engineering and Management, 21(6), 748-760. https://doi.org/10.3846/13923730.2014.893908

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