264 citations · 1.1k across the 18 of their papers we have counts for
5 papers · 1 filter
Cohort comfort models -- Using occupants' similarity to predict personal thermal preference with less data
Matias Quintana, Stefano Schiavon, Federico Tartarini +2
We introduce Cohort Comfort Models, a new framework for predicting how new occupants would perceive their thermal environment. Cohort Comfort Models leverage historical data collec…
Targeting occupant feedback using digital twins: Adaptive spatial-temporal thermal preference sampling to optimize personal comfort models
Mahmoud Abdelrahman, Clayton Miller
Collecting intensive longitudinal thermal preference data from building occupants is emerging as an innovative means of characterizing the performance of buildings and the people w…
Using Google Trends as a proxy for occupant behavior to predict building energy consumption
Chun Fu, Clayton Miller
In recent years, the availability of larger amounts of energy data and advanced machine learning algorithms has created a surge in building energy prediction research. However, one…
Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Vec
Mahmoud Abdelrahman, Adrian Chong, Clayton Miller
Conventional thermal preference prediction in buildings has limitations due to the difficulty in capturing all environmental and personal factors. New model features can improve th…
Balancing thermal comfort datasets: We GAN, but should we?
Matias Quintana, Stefano Schiavon, Kwok Wai Tham +1
Thermal comfort assessment for the built environment has become more available to analysts and researchers due to the proliferation of sensors and subjective feedback methods. Thes…