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Determining the cost optimum among a discrete set of building technologies to satisfy stringent energy targets

Published online by Cambridge University Press:  07 October 2015

Brian Simmons ,
Matthias H.Y. Tan ,
C.F. Jeff Wu  and
Godfried Augenbroe
Brian Simmons*
Affiliation:
Georgia Institute of Technology, Atlanta, Georgia, USA
Matthias H.Y. Tan
Affiliation:
Georgia Institute of Technology, Atlanta, Georgia, USA
C.F. Jeff Wu
Affiliation:
Georgia Institute of Technology, Atlanta, Georgia, USA
Godfried Augenbroe
Affiliation:
Georgia Institute of Technology, Atlanta, Georgia, USA
*
Reprint requests to: Brian Simmons, 11 Barrows Street, Apartment 6A, Boston, MA 02134. E-mail: brian.simmons@retroficiency.com
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Abstract

This paper presents the development of an optimization methodology for selecting the lowest monetary cost combinations of building technologies to meet set operational energy reduction targets. The new optimization algorithm introduced in this paper departs from the notion that optimal design choices over a large set of design parameters and properties can be driven by energy targets. We assume that design parameters are determined by many concurrent considerations fighting over the attention span of the design team. Our approach starts from a design outcome and asks the question, which set of discrete technologies are the right mix to reach an energy target in the cost optimal way? Such an approach has to face the challenge that the properties of market-available building technologies have a discrete nature that makes their optimal selection a combinatorial problem. The optimization algorithm searches the discrete combinatoric space by maximizing the following objective function: calculated energy savings divided by premium cost, where cost is defined as the additional cost over a baseline solution. The algorithm is codified into a custom MATLAB script and when compared to prescriptive methodologies is shown to be more cost effective and generically applicable given a palette of building technology alternatives and their corresponding cost data.

Keywords

Architectural DesignBuilding SimulationComputational DesignDesign AlternativesDesign Decision MakingDesign OptimizationOptimization Algorithm
Type
Special Issue Articles
Information
AI EDAM , Volume 29 , Special Issue 4: Generative and evolutionary design exploration , November 2015 , pp. 417 - 427
Copyright
Copyright © Cambridge University Press 2015 

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