An approach for building design optimization using design of experiments
Name
12273_2016_Article_334.xml.Meta
Size
9.22 KB
Format
XML
Checksum (MD5)
9cb4647f42c744f67a2adfc20864a9c8
Author(s)
Dhariwal, Jay
Banerjee, Rangan
Date Issued
November 2016
Journal
Building Simulation
Publisher
Tsinghua University Press
Citation
Dhariwal, Jay, and Rangan Banerjee. “An Approach for Building Design Optimization Using Design of Experiments.” Building Simulation 10, no. 3 (November 8, 2016): 323–336.
Version
Author's final manuscript
Abstract
Building simulation based optimization involves direct coupling of the optimization algorithm to a simulation model, making it computationally intensive. To overcome this issue, an approach is proposed using a combination of experimental design techniques (fractional factorial design and response surface methodology). These techniques approximate the simulation model behavior using surrogate models, which are several orders of magnitude faster than the simulation model. Fractional factorial design is used to identify the significant design variables. Response surface methodology is used to create surrogate models for the annual cooling and lighting energy with the screened significant variables. The error for these models is less than 10%, validating their effectiveness. These surrogate models speed up optimization with genetic algorithms, for single- and multi-objective optimization problems and scenario analyses, resulting in a better solution. Thus, optimization becomes possible within reasonable computational time with the proposed methodology. This framework is illustrated using the case study of a three-storey office building for New Delhi.
MIT Department
Massachusetts Institute of Technology. Department of Architecture
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s12273-016-0334-z
