Improved Thermal Comfort Model Leveraging Conditional Tabular GAN Focusing on Feature Selection
- The indoor thermal comfort in both homes and workplaces significantly influences the health and productivity of inhabitants. The heating system, controlled by Artificial Intelligence (AI), can automatically calibrate the indoor thermal condition by analyzing various physiological and environmental variables. To ensure a comfortable indoor environment, smart home systems can adjust parameters related to thermal comfort based on accurate predictions of inhabitants’ preferences. Modeling personal thermal comfort preferences poses two significant challenges: the inadequacy of data and its high dimensionality. An adequate amount of data is a prerequisite for training efficient machine learning (ML) models. Additionally, high-dimensional data tends to contain multiple irrelevant and noisy features, which might hinder ML models’ performance. To address these challenges, we propose a framework for predicting personal thermal comfort preferences, combining the conditional tabular generative adversarial network (CTGAN) with multiple feature selection techniques. We first address the data inadequacy challenge by applying CTGAN to generate synthetic data samples, incorporating challenges associated with multimodal distributions and categorical features. Then, multiple feature selection techniques are employed to identify the best possible sets of features. Experimental results based on a wide range of settings on a standard dataset demonstrated state-of-the-art performance in predicting personal thermal comfort preferences. The results also indicated that ML models trained on synthetic data achieved significantly better performance than models trained on real data. Overall, our method, combining CTGAN and feature selection techniques, outperformed existing known related work in thermal comfort prediction in terms of multiple evaluation metrics, including area under the curve (AUC), Cohen’s Kappa, and accuracy. Additionally, we presented a global, model-agnostic explanation of the thermal preference prediction system, providing an avenue for thermal comfort experiment designers to consciously select the data to be collected.
Document Type: | Article |
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Language: | English |
Author: | Md Shajalal, Milad Bohlouli, Hari Prasanna Das, Alexander Boden, Gunnar Stevens |
Parent Title (English): | IEEE Access |
Volume: | 12 |
First Page: | 30039 |
Last Page: | 30053 |
ISSN: | 2169-3536 |
URN: | urn:nbn:de:hbz:1044-opus-78244 |
DOI: | https://doi.org/10.1109/ACCESS.2024.3366453 |
Publisher: | IEEE |
Publishing Institution: | Hochschule Bonn-Rhein-Sieg |
Date of first publication: | 2024/02/14 |
Copyright: | 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. |
Funding: | This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955422. |
Keyword: | data inadequacy Personal thermal comfort; feature selection; generative adversarial network; machine learning |
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: | 2024/02/27 |
Licence (German): | Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |