The Squeaky wheel: Machine learning for anomaly detection in subjective thermal comfort votes

Title
The Squeaky wheel: Machine learning for anomaly detection in subjective thermal comfort votes
Authors
Keywords
Thermal comfort, Subjective votes, ASHRAE global thermal comfort database, Anomaly detection, K-nearest neighbors, Multivariate Gaussian, Occupancy responsive controls
Journal
BUILDING AND ENVIRONMENT
Volume 151, Issue -, Pages 219-227
Publisher
Elsevier BV
Online
2019-01-31
DOI
10.1016/j.buildenv.2019.01.050

Ask authors/readers for more resources

Reprint

Contact the author

Become a Peeref-certified reviewer

The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.

Get Started

Ask a Question. Answer a Question.

Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.

Get Started