<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhe Wang</style></author><author><style face="normal" font="default" size="100%">Thomas Parkinson</style></author><author><style face="normal" font="default" size="100%">Peixian Li</style></author><author><style face="normal" font="default" size="100%">Borong Lin</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Squeaky wheel: Machine learning for anomaly detection in subjective thermal comfort votes</style></title><secondary-title><style face="normal" font="default" size="100%">Building and Environment</style></secondary-title><short-title><style face="normal" font="default" size="100%">Building and Environment</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">anomaly detection</style></keyword><keyword><style  face="normal" font="default" size="100%">ASHRAE global thermal comfort database</style></keyword><keyword><style  face="normal" font="default" size="100%">K-nearest neighbors</style></keyword><keyword><style  face="normal" font="default" size="100%">Multivariate Gaussian</style></keyword><keyword><style  face="normal" font="default" size="100%">Occupancy responsive controls</style></keyword><keyword><style  face="normal" font="default" size="100%">Subjective votes</style></keyword><keyword><style  face="normal" font="default" size="100%">thermal comfort</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://linkinghub.elsevier.com/retrieve/pii/S0360132319300861</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">151</style></volume><pages><style face="normal" font="default" size="100%">219 - 227</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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&#039; 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;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.&lt;/p&gt;</style></abstract></record></records></xml>