Liu, Yiqiao and Chong, Wen Tong and Yau, Yat Huang and Wu, Jinshun and Chang, Yufan and Cui, Tong and Chang, Li and Pan, Song (2024) A hybrid learning approach to model the diversity of window-opening behavior. Building and Environment, 257. p. 111525. ISSN 0360-1323, DOI https://doi.org/10.1016/j.buildenv.2024.111525.
Full text not available from this repository.Abstract
The diverse window-opening behaviors of individuals can result in significant differences in indoor thermal environments, air quality, and energy utilization. However, the majority of existing studies focus on constructing an average window operation model, thus overlooking the diversity of behaviors. Current methods for addressing behavioral diversity face challenges with integration into building performance simulation software and are highly dependent on data scale. To address these limitations, this study proposes a novel approach that combines unsupervised learning (K-Means) and supervised learning (Light Gradient Boosting Machine, LightGBM) for modeling the diverse window-opening behaviors. Furthermore, the SHapley Additive exPlanations (SHAP) was employed to interpret the predictive model. This study yielded four key findings: 1) There were 12 different window-opening behavior patterns. Interestingly, 65 % of the residents ` window-opening behaviors were not influenced by environmental factors but were instead a matter of personal habit. 2) Using random sampling to divide the dataset may pose a risk of data leakage. The time series cross-validation method is more suitable for evaluating the performance of the window state prediction model. 3) Under the time series sampling strategy, the LightGBM model incorporating behavioral diversity improved the prediction accuracy by 1.3% - 10.4 % compared to the standalone LightGBM model. Notably, when the daily average window opening time was used as a clustering feature in the LightGBM model (Cluster(T)-LightGBM), the accuracy reached 87.1 %. 4) The SHAP feature analysis highlighted high-intensity window-opening categories, outdoor temperature, and indoor CO 2 concentration as the most pivotal predictors.
| Item Type: | Article |
|---|---|
| Funders: | Key Laboratory for Comprehensive Energy Saving of Cold Regions Architecture of Ministry of Education, Five-Year National Science and Technology Major Project of China (2016YFC0801706) ; (2017YFC0702202), National Natural Science Foundation of China (NSFC) (51578011), Hebei Province International Science and Technology Cooperation Fundamental Project at the North China Institute of Science and Technology (20594501D) |
| Uncontrolled Keywords: | Window-opening behavior; Behavioral diversity; K-Means; Light gradient boosting machine; SHapley additive exPlanations |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TH Building construction |
| Divisions: | Faculty of Engineering > Department of Mechanical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 25 Sep 2024 07:48 |
| Last Modified: | 25 Sep 2024 07:48 |
| URI: | http://eprints.um.edu.my/id/eprint/45188 |
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