<?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%">Joyce Kim</style></author><author><style face="normal" font="default" size="100%">Stefano Schiavon</style></author><author><style face="normal" font="default" size="100%">Gail Brager</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Personal comfort models &amp;ndash; A new paradigm in thermal comfort for occupant-centric environmental control</style></title><secondary-title><style face="normal" font="default" size="100%">Building and Environment</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.buildenv.2018.01.023</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">132</style></volume><pages><style face="normal" font="default" size="100%">114-124</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A&amp;nbsp;&lt;em&gt;personal comfort model&lt;/em&gt;&amp;nbsp;is a new approach to thermal comfort modeling that predicts an individual's thermal comfort response, instead of the average response of a large population. It leverages the&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/engineering/internet-of-things&quot; title=&quot;Learn more about Internet of Things from ScienceDirect's AI-generated Topic Pages&quot;&gt;Internet of Things&lt;/a&gt;&amp;nbsp;and machine learning to learn individuals' comfort requirements directly from the data collected in their everyday environment. Its results could be aggregated to predict comfort of a population. To provide guidance on future efforts in this emerging research area, this paper presents a unified framework for personal comfort models. We first define the problem by providing a brief discussion of existing thermal comfort models and their limitations for&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/engineering/real-world-application&quot; title=&quot;Learn more about Real World Application from ScienceDirect's AI-generated Topic Pages&quot;&gt;real-world applications&lt;/a&gt;, and then review the current state of research on personal comfort models including a summary of key advances and gaps. We then describe a modeling framework to establish fundamental concepts and methodologies for developing and evaluating personal comfort models, followed by a discussion of how such models can be integrated into indoor environmental controls. Lastly, we discuss the challenges and opportunities for applications of personal comfort models for&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/earth-and-planetary-sciences/architectural-design&quot; title=&quot;Learn more about Architectural Design from ScienceDirect's AI-generated Topic Pages&quot;&gt;building design&lt;/a&gt;, control, standards, and future research.</style></abstract></record></records></xml>