Building Simulation

, Volume 10, Issue 3, pp 323–336

An approach for building design optimization using design of experiments

Research Article Building Thermal, Lighting, and Acoustics Modeling

DOI: 10.1007/s12273-016-0334-z

Cite this article as:
Dhariwal, J. & Banerjee, R. Build. Simul. (2017) 10: 323. doi:10.1007/s12273-016-0334-z
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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.

Keywords

factorial design sensitivity analysis response surface methodology surrogate modelling genetic algorithms optimization 

Copyright information

© Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Energy Science and EngineeringIndian Institute of Technology BombayMumbaiIndia
  2. 2.Department of ArchitectureMassachusetts Institute of TechnologyCambridgeUSA

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