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Analyzing the results of buildings energy audit by using grey incidence analysis

Analyzing the results of buildings energy audit by using grey incidence analysis Purpose – The purpose of this paper is to analyse the results of energy audit reports and defines most favourable characteristics of system, which is energy consumption of buildings, and most favourable factors affecting these characteristics in order to modify and improve them. Design/methodology/approach – Grey set theory has the advantage of using fewer data to analyse many factors, and it is therefore more appropriate for system study rather than traditional statistical regression which requires massive data, normal distribution in the data and few variant factors. So, in this paper grey clustering and entropy of coefficient vector of grey evaluations are used to analyse energy consumption in buildings of the Oil Ministry in Tehran. Grey clustering in this study has been used for two purposes: First, all the variables of building relate to energy audit cluster in two main groups of indicators and the number of variables is reduced. Second, grey clustering with variable weights has been used to classify all buildings in three categories named “no standard deviation”, “low standard deviation” and “non-standard”. Entropy of coefficient vector of grey evaluations is calculated to investigate greyness of results. Findings – According to the results of the model, “the real building load coefficient” has been selected as the most important system characteristic and “uncontrolled area of the building” has been diagnosed as the most favourable factor which has the greatest effect on energy consumption of building. Research limitations/implications – Clustering greyness of 13 buildings is less than 0.5 and average uncertainly of clustering results is 66 per cent. Practical implications – It shows that among the 38 buildings surveyed in terms of energy consumption, three cases are in standard group, 24 cases are in “low standard deviation” group and 11 buildings are completely non-standard. Originality/value – In this research, a comprehensive analysis of the audit reports is proposed. This analysis helps the improvement of future audits, and assists in making energy conservation policies by studying the behaviour of system characteristic and related factors. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Grey Systems Theory and Application Emerald Publishing

Analyzing the results of buildings energy audit by using grey incidence analysis

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References (14)

Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
2043-9377
DOI
10.1108/GS-01-2014-0002
Publisher site
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Abstract

Purpose – The purpose of this paper is to analyse the results of energy audit reports and defines most favourable characteristics of system, which is energy consumption of buildings, and most favourable factors affecting these characteristics in order to modify and improve them. Design/methodology/approach – Grey set theory has the advantage of using fewer data to analyse many factors, and it is therefore more appropriate for system study rather than traditional statistical regression which requires massive data, normal distribution in the data and few variant factors. So, in this paper grey clustering and entropy of coefficient vector of grey evaluations are used to analyse energy consumption in buildings of the Oil Ministry in Tehran. Grey clustering in this study has been used for two purposes: First, all the variables of building relate to energy audit cluster in two main groups of indicators and the number of variables is reduced. Second, grey clustering with variable weights has been used to classify all buildings in three categories named “no standard deviation”, “low standard deviation” and “non-standard”. Entropy of coefficient vector of grey evaluations is calculated to investigate greyness of results. Findings – According to the results of the model, “the real building load coefficient” has been selected as the most important system characteristic and “uncontrolled area of the building” has been diagnosed as the most favourable factor which has the greatest effect on energy consumption of building. Research limitations/implications – Clustering greyness of 13 buildings is less than 0.5 and average uncertainly of clustering results is 66 per cent. Practical implications – It shows that among the 38 buildings surveyed in terms of energy consumption, three cases are in standard group, 24 cases are in “low standard deviation” group and 11 buildings are completely non-standard. Originality/value – In this research, a comprehensive analysis of the audit reports is proposed. This analysis helps the improvement of future audits, and assists in making energy conservation policies by studying the behaviour of system characteristic and related factors.

Journal

Grey Systems Theory and Application – Emerald Publishing

Published: Oct 28, 2014

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