Loughborough University
Browse
BS2019-Submission-Final.pdf (441.51 kB)

Applying desirability functions to preference modelling in low-energy building design optimization

Download (441.51 kB)
conference contribution
posted on 2019-07-02, 10:32 authored by Elaine Robinson, Christina J. Hopfe, M. Emmerich, I. Yevseyeva, Jonathan A. Wright
Building performance optimization is a valuable aid to design decision-making. Most existing research takes an ‘a posteriori’ approach, where stakeholder preferences are considered after deriving optimised results. Whilst this approach yields technically optimal solutions, it overlooks sub-optimal solutions that still satisfy stakeholder preferences. This research develops a technique to incorporate preferences into optimization by applying a “desirability function” to each criterion for multiple stakeholders. The approach enables the tradeoffs between decision-makers to be visualised as a Pareto frontier and aids “democratic” decision-making. Hence, incorporating preferences in advance of optimization may increase the likelihood of finding a desirable solution.

History

School

  • Architecture, Building and Civil Engineering

Published in

16th IBPSA International Conference & Exhibition Building Simulation

Citation

ROBINSON, E. ... et al., 2019. Applying desirability functions to preference modelling in low-energy building design optimization. Presented at the 16th IBPSA International Conference & Exhibition Building Simulation 2019, Rome, 2-4th Sept.

Publisher

IBPSA

Version

  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

2019-05-06

Publication date

2019

Language

  • en

Location

Rome

Usage metrics

    Loughborough Publications

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC