Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning

By Joyce Kim, Yuxun Zhou, Stefano Schiavon, Paul Raftery, Gail Brager. 2018

441 citations recorded by the source corpus.

Subfields: Building and Construction, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/2cd59efbc3454a87ae9adf5cce16df0d74b8a6f7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Shichao Liu
2. Stefano Schiavon
3. Toby Cheung
4. Gail Brager

In Environmental Engineering

1. Shichao Liu
2. Stefano Schiavon
3. Toby Cheung
4. Gail Brager

Citations in the corpus

• Analysis of the accuracy on PMV – PPD model using the ASHRAE Global Thermal Comfort Database II (2019, result): “Such an approach has demonstrated very high prediction accuracy well beyond that of the PMV-PPD model [45].”
• Personal thermal comfort models with wearable sensors (2019, methodology): “A personal comfort model using only control behavior of a personal chair system can generate a prediction AUC of 69% compared to approximately 53% (almost random) for the PMV and adaptive model [26].”