Prediction of government-owned building energy consumption based on an RReliefF and support vector machine model
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 developmentHow to Cite
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Copyright (c) 2015 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Copyright (c) 2015 The Author(s). Published by Vilnius Gediminas Technical University.
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This work is licensed under a Creative Commons Attribution 4.0 International License.