TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Shajalal, Md
A1  - Bohlouli, Milad
A1  - Das, Hari Prasanna
A1  - Boden, Alexander
A1  - Stevens, Gunnar
T1  - Focus on what matters: improved feature selection techniques for personal thermal comfort modelling
T2  - BuildSys '22: The 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. Boston, Massachusetts, November 9 - 10, 2022
N2  - Occupants' personal thermal comfort (PTC) is indispensable for their well-being, physical and mental health, and work efficiency. Predicting PTC preferences in a smart home can be a prerequisite to adjusting the indoor temperature for providing a comfortable environment. In this research, we focus on identifying relevant features for predicting PTC preferences. We propose a machine learning-based predictive framework by employing supervised feature selection techniques. We apply two feature selection techniques to select the optimal sets of features to improve the thermal preference prediction performance. The experimental results on a public PTC dataset demonstrated the efficiency of the feature selection techniques that we have applied. In turn, our PTC prediction framework with feature selection techniques achieved state-of-the-art performance in terms of accuracy, Cohen's kappa, and area under the curve (AUC), outperforming conventional methods.
KW  - thermal comfort modelling
KW  - Machine Learning
KW  - feature selection
SN  - 978-1-4503-9890-9
SB  - 978-1-4503-9890-9
U6  - https://doi.org/10.1145/3563357.3567406
DO  - https://doi.org/10.1145/3563357.3567406
SP  - 496
EP  - 499
S1  - 4
PB  - Association for Computing Machinery
CY  - New York, NY, United States
ER  -