Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Vec

by   Mahmoud Abdelrahman, et al.

Conventional thermal preference prediction in buildings has limitations due to the difficulty in capturing all environmental and personal factors. New model features can improve the ability of a machine learning model to classify a person's thermal preference. The spatial context of a building can provide information to models about the windows, walls, heating and cooling sources, air diffusers, and other factors that create micro-environments that influence thermal comfort. Due to spatial heterogeneity, it is impractical to position sensors at a high enough resolution to capture all conditions. This research aims to build upon an existing vector-based spatial model, called Build2Vec, for predicting spatial-temporal occupants' indoor environmental preferences. Build2Vec utilizes the spatial data from the Building Information Model (BIM) and indoor localization in a real-world setting. This framework uses longitudinal intensive thermal comfort subjective feedback from smart watch-based ecological momentary assessments (EMA). The aggregation of these data is combined into a graph network structure (i.e., objects and relations) and used as input for a classification model to predict occupant thermal preference. The results of a test implementation show 14-28 improvement over a set of baselines that use conventional thermal preference prediction input variables.


page 1

page 3

page 4

page 6

page 7

page 12

page 13

page 14


Humans-as-a-sensor for buildings: Intensive longitudinal indoor comfort models

Evaluating and optimising human comfort within the built environment is ...

Cohort comfort models – Using occupants' similarity to predict personal thermal preference with less data

We introduce Cohort Comfort Models, a new framework for predicting how n...

Enhancing personalised thermal comfort models with Active Learning for improved HVAC controls

Developing personalised thermal comfort models to inform occupant-centri...

Personalized local heating neutralizing individual, spatial and temporal thermo-physiological variances in extreme cold environments

In this paper, we investigate the feasibility, robustness and optimizati...

Comfort-as-a-Service: Designing a User-Oriented Thermal Comfort Artifact for Office Buildings

Most people spend up to 90 the field of facility management and related...

Building Matters: Spatial Variability in Machine Learning Based Thermal Comfort Prediction in Winters

Thermal comfort in indoor environments has an enormous impact on the hea...

Please sign up or login with your details

Forgot password? Click here to reset