Data-driven approach to optimal control of ACC systems and layout design in large rooms with thermal comfort consideration by using PSO

Yan Qiao, Si Wei Zhang, Nai Qi Wu, Xu Wang, Zhi Wu Li, Meng Chu Zhou, Ting Qu

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


In recent years, there are increasing concerns in energy savings for buildings since they are responsible for a large proportion of energy use. A public room in buildings could hold a number of persons who may prefer dissimilar thermal environment. Furthermore, different areas in such rooms may have different temperatures. Also, facility layout in such a room has effect on the distribution of the people in the room. Thus, it may affect its thermal environment and energy consumption as well. It is meaningful and challenging to effectively operate an air-conditioning control (ACC) system by taking the above mentioned factors into account such that the thermal environment is improved and energy is saved. With the lack of research reports on this issue, this work aims at optimally and dynamically controlling the set-point temperature of an ACC system and designing the facility layout so as to maximize the total thermal satisfaction rate (TSR) as well as energy savings. To do so, a non-linear mathematical programming model is proposed to optimize TSR by determining the set-point of an ACC system and the room layout. Then, a particle swarm optimization (PSO) algorithm is constructed to find an optimal or near optimal solution since it is hard to solve a non-linear mathematical programming problem in a reasonable time. Besides, for further energy saving, two more mathematical programming models are proposed to find a set-point of an ACC system under a given outside temperature and room layout determined by the PSO algorithm. Finally, by using a large library room at Macau University of Science and Technology (MUST) as a case, investigations with a large number of experiments are conducted to collect necessary data. Based on the data, regression analysis is done to predict its indoor temperatures in different areas and TSR at a given temperature. Numerical results show that, by the proposed method, it can improve the thermal satisfaction rate by about 27% and save the daily power cost by about 24.3% in comparison with the currently used manual control method.

Original languageEnglish (US)
Article number117578
JournalJournal of Cleaner Production
StatePublished - Nov 1 2019

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • General Environmental Science
  • Strategy and Management
  • Industrial and Manufacturing Engineering


  • Building
  • Energy savings
  • PSO
  • Thermal sensation


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