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

arXiv: https://arxiv.org/abs/2111.00199 · HTML: https://mlsanity.com/paper/2111.00199/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 56 references that are in the map.
- First posted on arXiv 30 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Balancing thermal comfort datasets: We GAN, but should we?](https://mlsanity.com/paper/2009.13154/) (2020)
- [Humans-as-a-sensor for buildings: Intensive longitudinal indoor comfort models](https://mlsanity.com/paper/2007.02014/) (2020)
- [Build2Vec: Building Representation in Vector Space](https://mlsanity.com/paper/2007.00740/) (2020)
- [Fast Neighborhood Graph Search using Cartesian Concatenation](https://mlsanity.com/paper/1312.3062/) (2013)
- [Spacematch: Using environmental preferences to match occupants to suitable activity-based workspaces](https://mlsanity.com/paper/2006.09570/) (2020)

## Built on by (most cited first, 4 of 4)

- [What is a Digital Twin Anyway? Deriving the Definition for the Built Environment from over 15,000 Scientific Publications](https://mlsanity.com/paper/2409.19005/) (2024)
- [Targeting occupant feedback using digital twins: Adaptive spatial-temporal thermal preference sampling to optimize personal comfort models](https://mlsanity.com/paper/2202.10707/) (2022)
- [Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments](https://mlsanity.com/paper/2404.16074/) (2024)
- [From Thermal Preference Prediction to Adaptive Thermal Intervention: A Reinforcement Learning Approach Using Physiological and Environmental Sensing](https://mlsanity.com/paper/2608.20423/) (2026)

## Abstract (arXiv)

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% accuracy improvement over a set of baselines that use conventional thermal preference prediction input variables.