{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T06:52:38Z","timestamp":1789541558229,"version":"build-2803163510"},"reference-count":31,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2017,12,11]],"date-time":"2017-12-11T00:00:00Z","timestamp":1512950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Carbon Management Fund of the University of Southampton"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2018,5,31]]},"abstract":"<jats:p>\n            In this article, we address the interrelated challenges of predicting user comfort and using this to reduce energy consumption in smart heating, ventilation, and air conditioning (HVAC) systems. At present, such systems use simple models of user comfort when deciding on a set-point temperature. Being built using broad population statistics, these models generally fail to represent individual users\u2019 preferences, resulting in poor estimates of the users\u2019 preferred temperatures. To address this issue, we propose the Bayesian Comfort Model (BCM). This\n            <jats:italic>personalised<\/jats:italic>\n            thermal comfort model uses a Bayesian network to learn from a user\u2019s feedback, allowing it to adapt to the users\u2019 individual preferences over time. We further propose an alternative to the ASHRAE 7-point scale used to assess user comfort. Using this model, we create an optimal HVAC control algorithm that minimizes energy consumption while preserving user comfort. Through an empirical evaluation based on the ASHRAE RP-884 dataset and data collected in a separate deployment by us, we show that our model is consistently 13.2% to 25.8% more accurate than current models and how using our alternative comfort scale can increase our model\u2019s accuracy. Through simulations we show that using this model, our HVAC control algorithm can reduce energy consumption by 7.3% to 13.5% while decreasing user discomfort by 24.8% simultaneously.\n          <\/jats:p>","DOI":"10.1145\/3057730","type":"journal-article","created":{"date-parts":[[2017,12,13]],"date-time":"2017-12-13T14:50:37Z","timestamp":1513176637000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":27,"title":["A Comfort-Based Approach to Smart Heating and Air Conditioning"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7501-0439","authenticated-orcid":false,"given":"Frederik","family":"Auffenberg","sequence":"first","affiliation":[{"name":"University of Southampton, Southampton, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen","family":"Snow","sequence":"additional","affiliation":[{"name":"University of Southampton, Southampton, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Stein","sequence":"additional","affiliation":[{"name":"University of Southampton, Southampton, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex","family":"Rogers","sequence":"additional","affiliation":[{"name":"University of Oxford, Parks Road, Oxford"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2017,12,11]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"ASHRAE 55 2010. 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