4.7 Article

Optimizing the energy consumption in a residential building at different climate zones: Towards sustainable decision making

Journal

JOURNAL OF CLEANER PRODUCTION
Volume 233, Issue -, Pages 634-649

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2019.06.093

Keywords

Heating-cooling; Energy consumption; Electricity cost; Greenhouse gas emissions; Thermal resistance; Correlation coefficient

Funding

  1. School of Engineering of RMIT University

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Electricity dependent heating-cooling (H/C) system has increased rapidly to provide thermal comfort to the occupants. H/C is a major source of energy consumption in residential buildings around the world. Studies revealed that the thermal properties of building materials are converted into the same unit as thermal resistance (R) in the USA, Canada, The UK, Australia, and other countries. However, none of the previous studies have considered the adaptation of national insulation codes to reduce electricity cost and greenhouse gas (GHG) emissions with the correlations of energy consumption (Q) to thermal resistances (R) of the building envelop (A). Therefore, this study attempts to review the optimum energy consumption of a selected house due to H/C at different choices of national code standardized R values. Accurate envelop areas (A) of the selected house components are estimated by applying the Autodesk Revit Building Information Model (BIM) to use in the energy model. The case study critically evaluates the variation of electricity cost, GHG emissions, and selection of the optimized thermostat settings for energy savings in different climatic regions and seasons of a year. Different correlations are obtained using risk analysis software on energy consumptions with code recognized arrays of insulations. This sensitivity analysis result shows that the corelation coefficient of energy consumption with medium-low insulated building envelop (r = 0.44) is more significant than improved, maximum, medium-high, and minimum insulations (r = 0.14-0.29) which indicates the insulation range where more emphasis should be put to get an optimized solution. Thus the developed analytical framework supports inclusive decision-making by selecting appropriate kinds of insulation for the design of a sustainable house. (C) 2019 Elsevier Ltd. All rights reserved.

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