Artificial neural network models using thermal sensations and occupants’ behavior for predicting thermal comfort

Zhipeng Deng, Qingyan Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

71 Citations (Scopus)


It is important to create comfortable indoor environments for building occupants. This study developed artificial neural network (ANN) models for predicting thermal comfort in indoor environments by using thermal sensations and occupants’ behavior. The models were trained by data on air temperature, relative humidity, clothing insulation, metabolic rate, thermal sensations, and occupants’ behavior collected in ten offices and ten houses/apartments. The models were able to predict similar acceptable air temperature ranges in offices, from 20.6 °C (69℉) to 25 °C (77℉) in winter and from 20.6 °C (69℉) to 25.6 °C (78℉) in summer. The occupants’ behavior in multi-occupant offices was more complex, which would lead to a slightly different prediction of thermal comfort. Since the occupants of the houses/apartments were responsible for paying their energy bills, the comfortable air temperature in these residences was 1.7 °C (3.0℉) lower than that in the offices in winter, and 1.7 °C (3.0℉) higher in summer. The comfort zone obtained by the ANN model using thermal sensations in the ten offices was narrower than the comfort zone in ASHRAE Standard 55, but that obtained by the ANN model using behaviors was wider than the ASHRAE comfort zone. This investigation demonstrates alternative approaches to the prediction of thermal comfort.

Original languageEnglish
Pages (from-to)587-602
Number of pages16
JournalEnergy and Buildings
Publication statusPublished - 1 Sept 2018


  • Air temperature
  • Clothing level
  • Data collection
  • Indoor environment
  • Metabolic rate
  • Model training
  • Relative humidity

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Mechanical Engineering
  • Electrical and Electronic Engineering


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