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Multiple attribute decision making for HVAC&R systems selection

Authors
  • Shahrestani, Mehdi
Publication Date
Jan 01, 2013
Source
OpenGrey Repository
Keywords
Language
English
License
Unknown

Abstract

Buildings account for 40% of total energy consumption in the UK and more than 55% of this energy is used by heating, ventilation, air-conditioning and refrigeration (HVAC&R) systems. This significant energy demand and the ascending trend in utilising HVAC&R systems together with the global need to impose energy-efficiency measures underline the importance of selecting the most appropriate HVAC&R system in a design process. In the early stages of the design and construction of a building, the design engineer is responsible for considering various systems in the process of HVAC&R systems selection. Although a broad range of simulation tools is developed for performance evaluation of HVAC&R systems, none of them is capable of performing a decision making process for HVAC&R systems selection. Therefore, the contribution of this study to knowledge has been the development of a multiple attribute decision making tool for HVAC&R systems selection for office buildings in the UK. Firstly, a set of reference office buildings was developed as representative of the UK office building stock and one of them was selected for further study. Then, a set of common alternative HVAC&R systems was identified. The reference office building, assumed to be located in London, together with the alternative HVAC&R systems were simulated in the TRNSVS and their technical performance, economic aspects and environmental impacts were assessed. Finally, to choose the most appropriate system among the alternatives a fuzzy multiple attribute decision making method was used to formulate the process of decision making. The scope of this study was further extended by considering 18 climate regions in the UK together with the effect of climate change in the decision making process using the degree-days theory. In addition, the UK Government's electricity decarbonisation plans were integrated to the developed decision making model. Finally, the model was transferred into a computational tool with a user-friendly interface developed in Matlab. / EThOS - Electronic Theses Online Service / GB / United Kingdom

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