We introduce Cohort Comfort Models, a new framework for predicting how new occupants would perceive their thermal environment. Cohort Comfort Models leverage historical data collected from a sample population, who have some underlying preference similarity, to predict thermal preference responses of new occupants. Our framework is capable of exploiting available background information such as physical characteristics and one-time on-boarding surveys (satisfaction with life scale, highly sensitive person scale, the Big Five personality traits) from the new occupant as well as physiological and environmental sensor measurements paired with thermal preference responses. We implemented our framework in two publicly available datasets containing longitudinal data from 55 people, comprising more than 6,000 individual thermal comfort surveys. We observed that, a Cohort Comfort Model that uses background information provided very little change in thermal preference prediction performance but uses none historical data. On the other hand, for half and one third of each dataset occupant population, using Cohort Comfort Models, with less historical data from target occupants, Cohort Comfort Models increased their thermal preference prediction by 8~\% and 5~\% on average, and up to 36~\% and 46~\% for some occupants, when compared to general-purpose models trained on the whole population of occupants. The framework is presented in a data and site agnostic manner, with its different components easily tailored to the data availability of the occupants and the buildings. Cohort Comfort Models can be an important step towards personalization without the need of developing a personalized model for each new occupant.
翻译:我们引入了Cohort Comfort 模型,这是预测新住户如何看待其热环境的新框架。Cohort Comfort 模型利用从抽样人群中收集的历史数据,这些抽样人群具有一些基本的偏好相似性,可以预测新住户的热偏好反应。我们的框架能够利用现有的背景资料,如物理特征和一次性登船调查(满意生命规模、高度敏感的人的规模、大五人个个性格特征),以及较易对热偏好做出回应的生理和环境传感器测量。我们用两个公开的数据集实施了我们的框架,其中包含55人的长期数据,包括6 000多项个人热偏好调查。我们观察到,一个Cohort Comfort 模型使用背景信息,在热偏好预测性绩效方面几乎没有变化,但没有使用任何历史数据。另一方面,对于每个数据集的半数和三分之一,使用Cohort Cohort Comfort 模型,目标住户、Cohort Comfort 模型和部分的新历史数据。我们用两种公开的热偏好方法对55人的热偏好预测,在普通用户和整个用户的模型中,在普通用户和整个用户框架上,每个经过培训的模型需要。