Event Title
Conference Editor
Jianshun Zhang; Edward Bogucz; Cliff Davidson; Elizabeth Krietmeyer
Keywords:
Indoor environment, Model training, Data collection, Air temperature, Relative humidity
Location
Syracuse, NY
Event Website
http://ibpc2018.org/
Start Date
24-9-2018 3:30 PM
End Date
24-9-2018 5:00 PM
Description
It is important to create comfortable indoor environments for building occupants. This study developed neural network (NN) 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. The models were able to predict similar acceptable air temperature ranges in offices, from 20.6℃ to 25℃ in winter and from 20.6℃ to 25.6℃ in summer. The comfort zone obtained by the NN model using thermal sensations in the ten offices was narrower than the comfort zone in ASHRAE Standard 55, but that obtained by the NN model using behaviors was wider than the ASHRAE comfort zone. This investigation demonstrates alternative approaches to the prediction of thermal comfort.
Recommended Citation
Deng, Zhipeng and Chen, Qingyan, "Neural Network Models Using Thermal Sensations and Occupants’ Behavior for Predicting Thermal Comfort" (2018). International Building Physics Conference 2018. 4.
DOI
https://doi.org/10.14305/ibpc.2018.hf-1.04
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Neural Network Models Using Thermal Sensations and Occupants’ Behavior for Predicting Thermal Comfort
Syracuse, NY
It is important to create comfortable indoor environments for building occupants. This study developed neural network (NN) 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. The models were able to predict similar acceptable air temperature ranges in offices, from 20.6℃ to 25℃ in winter and from 20.6℃ to 25.6℃ in summer. The comfort zone obtained by the NN model using thermal sensations in the ten offices was narrower than the comfort zone in ASHRAE Standard 55, but that obtained by the NN model using behaviors was wider than the ASHRAE comfort zone. This investigation demonstrates alternative approaches to the prediction of thermal comfort.
https://surface.syr.edu/ibpc/2018/HF1/4
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