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Framework for energy-based decision support system (DSS) for residential construction projects

Framework for energy-based decision support system (DSS) for residential construction projects
住宅建设项目能源决策支持系统(DSS)框架
批准号:
505476-2016
负责人:
Gul, Mustafa
金额:
$3.24万
依托单位:
依托单位国家:
加拿大
项目类别:
Collaborative Research and Development Grants
财政年份:
2017
资助国家:
加拿大
项目状态:
已结题
起止时间:
2017-01-01 至 2018-12-31

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中文摘要
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英文摘要
Over the years, Canada's cold climate has been the primary driver of the established energy design requirements. While energy demands have been steadily increasing to provide better indoor living conditions, there has been little corresponding innovation with respect to the technology and systems used in the building envelope and heating and cooling system. Thus, the capital costs of construction projects have increased in order to meet these demands. Insufficient energy design procedures have further hindered practitioners during the design stages due to the lack of an innovative framework allowing the efficient exploration of different combinations of building envelope and heating/cooling systems. This research introduces a novel solution for energy efficiency improvement by incorporating energy studies with target costing techniques. The energy evaluation process is based on the direct energy impact of the proposed construction systems-the three level hierarchical systems; assembly systems, sub-assembly systems, and construction components-and their indirect impact due to the interchangeable interactions among the systems. Consequently, an internal comparative-based energy evaluation of all possible energy models that satisfy client's requirements and target cost will be established, and improved energy design along with detailed project estimate breakdown and a full list of project specifications will be generated. The introduction of energy factors and energy mathematical models will promote the exploration of all possible configurations of energy models according to a specific project design. Through the utilization of data mining techniques, the developed energy functions will automatically generate a list of all possible simulated results of the energy consumption of a project, and the rule-based analysis (RBA) will select the alternatives that yield the highest energy efficiency. Finally, the proposed energy management system will provide the basis needed to validate and update the developed TCEng, which, in turn, will guide the energy design for future residential projects.
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