%0 Journal Article %K Thermal comfort %K Anomaly detection %K Subjective votes %K ASHRAE global thermal comfort database %K K-nearest neighbors %K Multivariate Gaussian %K Occupancy responsive controls %A Zhe Wang %A Thomas Parkinson %A Peixian Li %A Borong Lin %A Tianzhen Hong %B Building and Environment %D 2019 %G eng %P 219 - 227 %R 10.1016/j.buildenv.2019.01.050 %T The Squeaky wheel: Machine learning for anomaly detection in subjective thermal comfort votes %U https://linkinghub.elsevier.com/retrieve/pii/S0360132319300861 %V 151 %8 03/2019 %! Building and Environment %X

Anomalous patterns in subjective votes can bias thermal comfort models built using data-driven approaches. A stochastic-based two-step framework to detect outliers in subjective thermal comfort data is proposed to address this problem. The anomaly detection technique involves defining similar conditions using a k-Nearest Neighbor (KNN) method and then quantifying the dissimilarity of the occupants' votes from their peers under similar thermal conditions through a Multivariate Gaussian approach. This framework is used to detect outliers in the ASHRAE Global Thermal Comfort Database I & II. The resulting anomaly-free dataset produced more robust comfort models avoiding dubious predictions. The proposed method has been proven to effectively distinguish outliers from inter-individual variabilities in thermal demand. The proposed anomaly detection framework could easily be applied to other applications with different variables or subjective metrics. Such a tool holds great promise for use in the development of occupancy responsive controls for automated building HVAC systems.