A Transfer Active Learning Framework to Predict Thermal Comfort

Annamalai Natarajan and Emil Laftchiev
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Publication Issue: 
Special Issue PHM for Human Health and Performance
Submission Type: 
Full Paper
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A Transfer Active Learning Framework to Predict Thermal Comfort
ijphm_19_003.pdf302.14 KBFebruary 21, 2019 - 8:28am

Personal thermal comfort is the feeling that individuals have about how hot, cold or comfortable they are. Studies have shown that thermal comfort is a key component of human performance in the work place and that personalized thermal comfort models can be learned from user labeled data that is collected from wearable devices and room sensors. These personalized thermal comfort models can then be used to optimize the thermal comfort of room occupants to maximize their performance. Unfortunately, personalized thermal comfort models can only be learned after extensive dataset collection and user labeling. This paper addresses this challenge by proposing a transfer active learning framework for thermal comfort prediction that reduces the burdensome task of collecting large labeled datasets for each new user. The framework leverages domain knowledge from prior users and an active learning strategy for new users that reduces the necessary size of the labeled dataset. When tested on a real dataset collected from five users, this framework achieves a 70% reduction in the required size of the labeled dataset as compared to the fully supervised learning approach. Specifically, the framework achieves a mean error of 0.82±0.05, while the supervised learning approach achieves a mean error of 0.85±0.04.

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Submission Keywords: 
thermal comfort
transfer learning
active learning
office work performance
Submission Topic Areas: 
Industrial applications
Modeling and simulation
Standards and methodologies
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