Cohort comfort models — Using occupant's similarity to predict personal thermal preference with less data. (January 2023)

• Record Type: Journal Article
• Authors: Quintana, Matias; Schiavon, Stefano; Tartarini, Federico; Kim, Joyce; Miller, Clayton
• Is Part Of: Building and environment. Volume 227:Part 1(2023)
• Publication Date: 2023-01
• Subjects: Thermal comfort -- Clustering -- Personalized environments -- Cold start -- Warm start -- Recommender systems; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2022.109685
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: Cohort Comfort Models (CCM) are introduced as a technique for creating a personalized thermal prediction for a new building occupant without the need to collect large amounts of individual comfort-related data. This approach leverages historical data collected from a sample population, who have some underlying preference similarity to the new occupant. The method uses background information such as physical and demographic characteristics and one-time onboarding surveys (satisfaction with life scale, highly sensitive person scale, personality traits) from the new occupant, as well as physiological and environmental sensor measurements paired with a few thermal preference responses. The framework was implemented using two personal comfort datasets containing longitudinal data from 55 people. The datasets comprise more than 6000 unique right-here-right-now thermal comfort surveys. The results show that a CCM that uses only the one-time onboarding survey information of an individual occupant has generally as good or better performance as compared to conventional general-purpose models, but uses no historical longitudinal data as compared to personalized models. If up to ten historical personal preference data points are used, CCM increased the thermal preference prediction by 8% on average and up to 36% for half of the occupants in the first of the tested datasets. In the second dataset, one-third of the occupants increased their thermal preference prediction by 5% on average and up to 46%. CCM can be an important step toward the development of personalized thermal comfort models without the need to collect a large number of datapoints per person. Highlights: Cohort comfort models (CCM) fill the gap between generalized and personalized models. CCM achieve similar or better performance as compared to generalized models. CCM have similar benefits to personalized models, but with less training data needed. Ten historical data points can increase performance by up to 46% for some occupants. Some occupants benefit more than others, which supports further work in this area.