01980nas a2200277   4500008004100000022001300041245009800054210006900152260001200221300001400233490000800247520110300255653002201358653004301380653002401423653002601447653003401473653002101507653002001528100001401548700002201562700001601584700001601600700001901616856006701635       2019                        eng d  a0360132300aThe Squeaky wheel: Machine learning for anomaly detection in subjective thermal comfort votes0 aSqueaky wheel Machine learning for anomaly detection in subjecti  c03/2019  a219 - 2270 v1513 a<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' 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>10aanomaly detection10aASHRAE global thermal comfort database10aK-nearest neighbors10aMultivariate Gaussian10aOccupancy responsive controls10aSubjective votes10athermal comfort1 aWang, Zhe1 aParkinson, Thomas1 aLi, Peixian1 aLin, Borong1 aHong, Tianzhen  uhttps://linkinghub.elsevier.com/retrieve/pii/S0360132319300861