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An ANN-exhaustive-listing method for optimization of multiple building shapes and envelope properties with maximum thermal performance

  • Yaolin LinEmail author
  • Wei Yang
Research Article
  • 4 Downloads

Abstract

With increasing awareness of sustainability, demands on optimized design of building shapes with a view to maximize its thermal performance have become stronger. Current research focuses more on building envelopes than shapes, and thermal comfort of building occupants has not been considered in maximizing thermal performance in building shape optimization. This paper attempts to develop an innovative ANN (artificial neural network)-exhaustive-listing method to optimize the building shapes and envelope physical properties in achieving maximum thermal performance as measured by both thermal load and comfort hour. After verified, the developed method is applied to four different building shapes in five different climate zones in China. It is found that the building shape needs to be treated separately to achieve sufficient accuracy of prediction of thermal performance and that the ANN is an accurate technique to develop models of discomfort hour with errors of less than 1.5%. It is also found that the optimal solutions favor the smallest window-to-external surface area with triplelayer low-E windows and insulation thickness of greater than 90 mm. The merit of the developed method is that it can rapidly reach the optimal solutions for most types of building shapes with more than two objective functions and large number of design variables.

Keywords

ANN (artificial neural network) exhaustivelisting building shape optimization thermal load thermal comfort 

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Notes

Acknowledgements

This work was supported by the Natural Science Foundation of Hubei Province (Grant No. 2017CFB602); Hunan Provincial Department of Housing and Urban Rural Development (Grant No. KY2016063); Wuhan Committee of Municipal and Rural Construction (Grant No. 2015191) and Wuhan University of Technology (Grant Nos. 40120171, 20410632, 20410646, and 35400206).

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mechanical and Automotive EngineeringShanghai University of Engineering ScienceShanghaiChina
  2. 2.School of Civil Engineering and ArchitectureWuhan University of TechnologyWuhanChina
  3. 3.College of Engineering and ScienceVictoria UniversityMelbourneAustralia

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