A multi-stage optimization of passively designed high-rise residential buildings in multiple building operation scenarios

Xi Chen, Hongxing Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

39 Citations (Scopus)

Abstract

Machine learning methods are employed to develop surrogate models for improving the computation efficiency of the multi-objective optimization process. The surrogate model is trained by modeling experiments with EnergyPlus and R, which can provide reliable energy performance indicators of generic building models featuring passive design parameters including the building layout, envelope thermophysics, building geometry and infiltration & air-tightness. The lighting, cooling and heating demands of the generic building model are determined by the hybrid ventilation and light diming control strategies in compliance with local green building assessment criteria in Hong Kong. The multiple linear regression (MLR), multivariate adaptive regression splines (MARS), and support vector machines (SVM) are examined by the statistical modelling. SVM is capable of fitting a surrogate model with the best prediction performance based on the coefficient of determination and root mean square error. In addition, both single-sided ventilation and cross-ventilation models under varied thermal load requirements are investigated to compare the preferable design solutions for each scenario. The multi-stage optimization approach is also applied to a Mediterranean climate to explore optimal design solutions in more diverse external environmental conditions. This research can provide a highly efficient design optimization tool to appropriately deploy passive architectural strategies in a green building project.
Original languageEnglish
Pages (from-to)541-557
Number of pages17
JournalApplied Energy
Volume206
DOIs
Publication statusPublished - 1 Jan 2017

Keywords

  • Energy demand
  • Green building
  • Machine learning
  • Passive design
  • Surrogate model

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Energy(all)
  • Mechanical Engineering
  • Management, Monitoring, Policy and Law

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