The Use of Wireless Systems and Assessment Models for the Sustainability of Intelligent Buildings

Article Preview

Abstract:

Sustainability assessment of intelligent buildings involves multiple indicators and parameters, multi-criteria decision-making and multiple variables model. In order to solve the problem: firstly, identify the key performance indicators of the sustainability related to intelligent buildings (environmental, social, economic and technological factors);optimize the selected indicators with both experts knowledge and measurable data; secondly, develop a new model for measuring the level of sustainability for intelligent buildings. The data acquisition of objective indicators for intelligent buildings is based upon the wireless wearable sensors networks, and the subjective indicators connected with expert experience derived through questionnaire surveys. Using a consensus-based model, which is analyzed by the analytical hierarchical process (AHP) for multi-criteria decision-making. Using the multi-attribute model based on structure entropy weight methodology and fuzzy comprehensive evaluation for priority and weight setting in the sustainability assessment is studied. It is concluded that the whole system not only acquires multiple data but also gets available and reliable assessment for the sustainability of intelligent buildings.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 953-954)

Pages:

1663-1667

Citation:

Online since:

June 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Clements-Croome DJ. Intelligent Buildings: Design, Management and Operation. London: Thomas Telford ; (2004).

Google Scholar

[2] Building Energy Research Center of Tsinghua University. Annual report on China building energy efficiency 2008. Beijing:China Building Industry Press.

Google Scholar

[3] CSJS. China urban construction information net, www. csjs. gov. cn (2005).

Google Scholar

[4] Arno Schlueter, Frank Thesseling. Building information model based energy/exergy performance assessment in early design stages. UK: Automation in Construction, 2008; 18(2): 153-163.

DOI: 10.1016/j.autcon.2008.07.003

Google Scholar

[5] Chen Z, D.J. Clements-Croome, Hong J, Li H, Xu Q. A multicriteria lifespan energy efficiency approach to intelligent building assessment. Energy and Buildings 2006; 38(5): 393-409.

DOI: 10.1016/j.enbuild.2005.08.001

Google Scholar

[6] Philomena M. Bluyssen, Sabine Janssen, Linde H. van den Brink, Yvonne de Kluizenaar. Assessment of wellbeing in an indoor office environment. Building and Environment, 2011; 46(12): 2632-2640.

DOI: 10.1016/j.buildenv.2011.06.026

Google Scholar

[7] Ma Yuan, Yu Junqi, Yang Chuangye, Wang Lei. Study on power energy consumption model for large-scale public building. 2010 2nd International Workshop on Intelligent Systems and Applications, ISA (2010).

DOI: 10.1109/iwisa.2010.5473608

Google Scholar

[8] Information on http: /www. zigbee. org.

Google Scholar

[9] Alwaer H., Clements-Croome DJ. Key performance indicators (KPIs) and priority setting in using the multi-attribute approach for assessing sustainable intelligent buildings. Building and Environment 2010; 45: 799-807.

DOI: 10.1016/j.buildenv.2009.08.019

Google Scholar

[10] Li, B. Assessing the influence of indoor environment on self-reported productivity in offices. Ph. D thesis, University of Reading, (1998).

Google Scholar

[11] Cheng Qiyue. Structure entropy weight method to confirm the weight of evaluating index. Systems Engineering-Theory & Practice vol. 30, 2010(7): 1225-1228 (In Chinese. ).

Google Scholar

[12] Ali Vakili-Ardebili, Abdel Halim Boussabaine. Application of fuzzy techniques to develop an assessment framework for building design eco-drivers. Building and Environment 2007; 42: 3785-3800.

DOI: 10.1016/j.buildenv.2006.11.017

Google Scholar