@article {31666,
	title = {The Squeaky wheel: Machine learning for anomaly detection in subjective thermal comfort votes},
	journal = {Building and Environment},
	volume = {151},
	year = {2019},
	month = {03/2019},
	pages = {219 - 227},
	abstract = {<p>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{\textquoteright} 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 \&amp; 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.</p>},
	keywords = {anomaly detection, ASHRAE global thermal comfort database, K-nearest neighbors, Multivariate Gaussian, Occupancy responsive controls, Subjective votes, thermal comfort},
	issn = {03601323},
	doi = {10.1016/j.buildenv.2019.01.050},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0360132319300861},
	author = {Zhe Wang and Thomas Parkinson and Peixian Li and Borong Lin and Tianzhen Hong}
}
