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Research Papers: Integrated Sustainable Equipment and Systems for Buildings

Examining the LEED Rating System Using Inverse Optimization

[+] Author and Article Information
Sarina D. O. Turner

e-mail: sarina@mie.utoronto.ca

Timothy C. Y. Chan

Assistant Professor
e-mail: tcychan@mie.utoronto.ca
Department of Mechanical and
Industrial Engineering,
University of Toronto,
Toronto, ON M5S 3G8, Canada

1Corresponding author.

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING. Manuscript received February 15, 2013; final manuscript received August 2, 2013; published online September 20, 2013. Assoc. Editor: Jorge E. Gonzalez.

J. Sol. Energy Eng 135(4), 040901 (Sep 20, 2013) (8 pages) Paper No: SOL-13-1057; doi: 10.1115/1.4025221 History: Received February 15, 2013; Revised August 02, 2013

The Leadership in Energy and Environmental Design (LEED) rating system is the most recognized green building certification program in North America. In order to be LEED certified, a building must earn a sufficient number of points, which are obtained through achieving certain credits or design elements. In LEED versions 1 and 2, each credit was worth one point. In version 3, the LEED system changed so that certain credits were worth more than one point. In this study, we develop an inverse optimization approach to examine how building designers intrinsically valued design elements in LEED version 2. Because of the change in the point system between version 2 and version 3, we aim to determine whether building designers actually valued each credit equally, and if not, whether their valuations matched the values in version 3. Due to the large dimensionality of the inverse optimization problem, we develop an approximation to improve tractability. We apply our method to 306 different LEED-certified buildings in the continental United States. We find that building designers did not value all credits equally and that other factors such as cost, building type, and size, and certification level play a role in how the credits are valued. Overall, inverse optimization may provide a new method to assess historical data and support the design of future versions of LEED.

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Figures

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Fig. 1

Distribution of buildings by type

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Fig. 2

Distribution of analyzed buildings by type and size

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Fig. 3

Distribution of analyzed buildings by geographical region [21]

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Fig. 4

Distribution of analyzed buildings by certification level achieved

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Fig. 5

Change in point values for analyzed buildings by type

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Fig. 6

Change in point values for analyzed buildings by type and size (10 K to 25 K sq. ft)

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Fig. 7

Change in point values for analyzed educational buildings by certification level achieved

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Fig. 8

A comparison of point value changes suggested by inverse optimization and LEED version 3

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