Addressing data inadequacy challenges in personal comfort models by combining pretrained comfort models

• Published in: Energy and buildings Vol. 264; p. 112068
• Main Authors: Zhang, Tianyu, Gu, Jake, Ardakanian, Omid, Kim, Joyce
• Format: Journal Article
• Language: English
• Published: Lausanne Elsevier B.V 01.06.2022; Elsevier BV
• Subjects: Artificial label; Clustering; Cold start conditions; Ensemble methods; Internet of Things; Office buildings; Personal comfort model; Personal comfort system; Preferences; Thermal comfort; Training; Transfer learning; Personal comfort model; Thermal comfort; Transfer learning; Personal comfort system; Artificial label; Ensemble methods; Internet of Things; Cold start conditions
• ISSN: 0378-7788; 1872-6178
• DOI: 10.1016/j.enbuild.2022.112068

Occupant thermal-comfort complaints are the biggest operational headache of facilities managers. Many of the complaints can be attributed to the diverse nature of individuals’ thermal comfort needs which are not accounted for in the de facto standard for thermal comfort. This has motivated research on developing data-driven personal comfort models and incorporating them in control loops. But the progress on this front has been hampered by the lack of sufficient ground-truth thermal comfort data to train accurate thermal comfort models. To address this problem, in this paper we explore how artificial labels, indicating individuals’ true thermal preference, can be generated from their heating and cooling behaviour with a personal comfort system. Furthermore, we use clustering to identify individuals with similar comfort requirements in a rich dataset collected from 37 individuals in an office building, and develop a small number of group comfort models, each achieving a high accuracy in predicting the thermal comfort of individuals within the respective cluster. The pretrained group comfort models are then combined using an ensemble method to create a general thermal comfort model that can accurately predict the thermal comfort of any individual without knowing their thermal preferences or group membership a priori. We evaluate the efficacy of two ensemble methods as more training data becomes available and show that they outperform two conventional comfort models (PMV, Adaptive) and the personal comfort model that is developed from scratch for a particular individual. Specifically, the best ensemble comfort model yields on average 71% accuracy in predicting individuals’ thermal preference using only 6 hours of training data, excluding no occupancy periods.