Passive design optimization of newly-built residential buildings in Shanghai for improving indoor thermal comfort while reducing building energy demand

Research output: Contribution to journalArticle

Abstract

The objective of this paper is to optimize the passive design of newly-built residential buildings in hot summer and cold winter region of China for improving indoor thermal comfort while reducing building energy demand. In this respect, this paper investigates the performance of a representative apartment building in the city of Shanghai and evaluates the optimum solutions by using a developed optimization approach, which includes three major steps of 1) setting the model for multi-objective optimization, 2) sensitivity analysis for reducing the dimension of input variables, and 3) multi-objective optimization by using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) coupled with the Artificial Neural Network (ANN), among which a novel indicator for evaluating the annual indoor thermal comfort of residential buildings of Shanghai named Comfort Time Ratio (CTR) is defined based on the modification of Szokolay's theory in terms of bioclimatic analysis, and the impacts of passive design variables on the indoor thermal comfort and building energy demand in terms of different directions are comprehensively investigated. Results of the multi-objective optimization indicate that the residential buildings of Shanghai have a great potential in comfort-improvement and energy-saving. A series of novel optimal passive design tactics for residential buildings in Shanghai are derived accordingly which could be easily understood and conveniently carried out by the architects in practice.

Details

Authors
  • Shaoqing Gou
  • Vahid M. Nik
  • Jean Louis Scartezzini
  • Qun Zhao
  • Zhengrong Li
Organisations
External organisations
  • Chalmers University of Technology
  • Swiss Federal Institute of Technology
  • Tongji University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Building Technologies

Keywords

  • Artificial neural network, Energy demand, Genetic algorithm, Multi-objective optimization, Passive design, Residential buildings, Thermal comfort
Original languageEnglish
Pages (from-to)484-506
Number of pages23
JournalEnergy and Buildings
Volume169
Publication statusPublished - 2018 Jun 15
Publication categoryResearch
Peer-reviewedYes