Methods for adaptive behaviors satisfaction assessment with energy efficient building design.
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| Title: | Methods for adaptive behaviors satisfaction assessment with energy efficient building design. |
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| Authors: | Shafaghat, Arezou1,2 arezou@utm.my, Keyvanfar, Ali2 akeyvanfar@utm.my, Abd. Majid, Muhd Zaimi2 mzaimi@utm.my, Lamit, Hasanuddin Bin1 b-hasanuddin@utm.my, Ahmad, Mohd Hamdan3 b-hamdan@utm.my, Ferwati, Mohamed Salim4 sferwati@qu.edu.qa, Ghoshal, Sib Krishna5 sibkrishna@utm.my |
| Source: | Renewable & Sustainable Energy Reviews. May2016, Vol. 57, p250-259. 10p. |
| Subjects: | Customer satisfaction, Building design & construction, Energy consumption of buildings, Energy industries, Energy economics |
| Abstract: | Efficient and accurate methods and models are ever-demanding to determining the user satisfaction in energy efficient buildings. Thus, several techniques are developed based on sustainable building assessment tools and standards to achieve such target. However, the user satisfaction from adaptive behavior in the design phase of buildings lifecycle is never addressed. This paper attempts to identify the most applicable data input method to measure the satisfaction from adaptive behavior with energy efficient buildings in their design phase. A comprehensive literature survey on all user satisfaction data input methods is conducted to serve the taxonomy by classifying them into two major clusters depending on performance and perception. Kano method to measure satisfaction perception from cognitive experience is demonstrated to be the most suitable user satisfaction data input method. Furthermore, Kano method can delicately assist energy efficient building design consultants to assess the user contentment requirements for sustainable building accreditation effectively. [ABSTRACT FROM AUTHOR] |
| Copyright of Renewable & Sustainable Energy Reviews is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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