عنوان

The Squeaky Wheel:

Number

LA3z1242jb

Title Proper

The Squeaky Wheel:

General Material Designation

[Article]

First Statement of Responsibility

Wang, Zhe; Parkinson, Thomas; Li, Peixian; Lin, Borong; Hong, Tianzhen

Title Proper by Another Author

Machine learning for anomaly detection in subjective thermal comfort votes

Text of Note

Anomalous patterns in subjective votes can bias thermal comfort models built using data-driven approaches. A stochastic-based two-step framework to detect outliers in subjective thermal comfort data is proposed to address this problem. The anomaly detection technique involves defining similar conditions using a k-Nearest Neighbor (KNN) method and then quantifying the dissimilarity of the occupants' votes from their peers under similar thermal conditions through a Multivariate Gaussian approach. This framework is used to detect outliers in the ASHRAE Global Thermal Comfort Database I & II. The resulting anomaly-free dataset produced more robust comfort models avoiding dubious predictions. The proposed method has been proven to effectively distinguish outliers from inter-individual variabilities in thermal demand. The proposed anomaly detection framework could easily be applied to other applications with different variables or subjective metrics. Such a tool holds great promise for use in the development of occupancy responsive controls for automated building HVAC systems.

Date of Publication

2019

Title

UC Berkeley

Electronic name

[Article]

275578

a

Y