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A Visual Analytics Based Decision Su...

Dutta, Ranojoy.

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A Visual Analytics Based Decision Support Methodology For Evaluating Low Energy Building Design Alternatives.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Visual Analytics Based Decision Support Methodology For Evaluating Low Energy Building Design Alternatives./
Author:	Dutta, Ranojoy.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2013,
Description:	150 p.
Notes:	Source: Masters Abstracts International, Volume: 52-04.
Contained By:	Masters Abstracts International52-04(E).
Subject:	Architecture. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1549076
ISBN:	9781303601484

A Visual Analytics Based Decision Support Methodology For Evaluating Low Energy Building Design Alternatives.
Dutta, Ranojoy.

A Visual Analytics Based Decision Support Methodology For Evaluating Low Energy Building Design Alternatives. - Ann Arbor : ProQuest Dissertations & Theses, 2013 - 150 p.

Source: Masters Abstracts International, Volume: 52-04.

Thesis (M.S.)--Arizona State University, 2013.

The ability to design high performance buildings has acquired great importance in recent years due to numerous federal, societal and environmental initiatives. However, this endeavor is much more demanding in terms of designer expertise and time. It requires a whole new level of synergy between automated performance prediction with the human capabilities to perceive, evaluate and ultimately select a suitable solution. While performance prediction can be highly automated through the use of computers, performance evaluation cannot, unless it is with respect to a single criterion. The need to address multi-criteria requirements makes it more valuable for a designer to know the "latitude" or "degrees of freedom" he has in changing certain design variables while achieving preset criteria such as energy performance, life cycle cost, environmental impacts etc. This requirement can be met by a decision support framework based on near-optimal "satisficing" as opposed to purely optimal decision making techniques. Currently, such a comprehensive design framework is lacking, which is the basis for undertaking this research.

ISBN: 9781303601484Subjects--Topical Terms:

523581
Architecture.

A Visual Analytics Based Decision Support Methodology For Evaluating Low Energy Building Design Alternatives.
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245 1 2 $a A Visual Analytics Based Decision Support Methodology For Evaluating Low Energy Building Design Alternatives.
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500 $a Source: Masters Abstracts International, Volume: 52-04.
500 $a Adviser: T Agami Reddy.
502 $a Thesis (M.S.)--Arizona State University, 2013.
520 $a The ability to design high performance buildings has acquired great importance in recent years due to numerous federal, societal and environmental initiatives. However, this endeavor is much more demanding in terms of designer expertise and time. It requires a whole new level of synergy between automated performance prediction with the human capabilities to perceive, evaluate and ultimately select a suitable solution. While performance prediction can be highly automated through the use of computers, performance evaluation cannot, unless it is with respect to a single criterion. The need to address multi-criteria requirements makes it more valuable for a designer to know the "latitude" or "degrees of freedom" he has in changing certain design variables while achieving preset criteria such as energy performance, life cycle cost, environmental impacts etc. This requirement can be met by a decision support framework based on near-optimal "satisficing" as opposed to purely optimal decision making techniques. Currently, such a comprehensive design framework is lacking, which is the basis for undertaking this research.
520 $a The primary objective of this research is to facilitate a complementary relationship between designers and computers for Multi-Criterion Decision Making (MCDM) during high performance building design. It is based on the application of Monte Carlo approaches to create a database of solutions using deterministic whole building energy simulations, along with data mining methods to rank variable importance and reduce the multi-dimensionality of the problem. A novel interactive visualization approach is then proposed which uses regression based models to create dynamic interplays of how varying these important variables affect the multiple criteria, while providing a visual range or band of variation of the different design parameters. The MCDM process has been incorporated into an alternative methodology for high performance building design referred to as Visual Analytics based Decision Support Methodology [VADSM]. VADSM is envisioned to be most useful during the conceptual and early design performance modeling stages by providing a set of potential solutions that can be analyzed further for final design selection. The proposed methodology can be used for new building design synthesis as well as evaluation of retrofits and operational deficiencies in existing buildings.
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