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Focus on what matters: improved feature selection techniques for personal thermal comfort modelling

  • 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.

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Metadaten
Document Type:Conference Object
Language:English
Author:Md Shajalal, Milad Bohlouli, Hari Prasanna Das, Alexander Boden, Gunnar Stevens
Parent Title (English):BuildSys '22: The 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. Boston, Massachusetts, November 9 - 10, 2022
Number of pages:4
First Page:496
Last Page:499
ISBN:978-1-4503-9890-9
DOI:https://doi.org/10.1145/3563357.3567406
Publisher:Association for Computing Machinery
Place of publication:New York, NY, United States
Date of first publication:2022/12/08
Copyright:Copyright © 2022 ACM. Abstracting with credit is permitted.
Funding:This research has been funded by the EU Horizon 2020 Marie Skłodowska-Curie International Training Network GECKO, Grant number 955422 (https:/gecko-project.eu/).
Keyword:Machine Learning; feature selection; thermal comfort modelling
Departments, institutes and facilities:Fachbereich Wirtschaftswissenschaften
Institut für Verbraucherinformatik (IVI)
Dewey Decimal Classification (DDC):3 Sozialwissenschaften / 38 Handel, Kommunikation, Verkehr / 380 Handel, Kommunikation, Verkehr
Entry in this database:2022/12/14