Faridah, Faridah and Utami, Sentagi Sesotya and Wijaya, Dinta Dwi Agung and Yanti, Ressy Jaya and Putra, Wahyu Sukestyastama and Adrian, Billie (2024) An indoor airflow distribution predictor using machine learning for a real-time healthy building monitoring system in the tropics. Building Services Engineering Research and Technology, 45 (3). 293 – 315. ISSN 01436244
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Abstract
Indoor air quality is the foundation of a good indoor environment. The COVID-19 pandemic further highlighted the importance of providing real-time airflow distribution information within the Building Environmental Monitoring System (BEMS) to minimize the risk of infectious airborne transmission. This paper discusses the process of developing a predictive model for indoor airflow distribution prediction with indoor and outdoor input parameters using machine learning and its implementation in healthy BEMS for a classroom in the tropical climate region of Yogyakarta, Indonesia. This paper encompassed field measurement and simulation involving outdoor climate conditions and the operational status of the classroom’s windows, Air Conditioning units, and fans. Three machine learning models were constructed using OLS, LASSO, and Ridge methods. Datasets for the modeling were generated from CFD model simulations in IES VE and were assessed for correlation. The mean temperature and velocity differences between the CFD model simulation and measurement results are 0.21°C and 0.083 m/s, respectively. Outdoor climate conditions and the operational status of the classroom’s utilities significantly influence the indoor airflow distribution characteristics. The three models indicate a relatively poor performance, where the classroom had a relatively low sensitivity to input changes. However, the best model performance was achieved using the LASSO method, with average values from post-normalization of (Formula presented.) and Root Mean Square Error (RMSE) of 0.336 and 0.077, respectively. The model was implemented in healthy BEMS on the “Platform for Healthy and Energy Efficient Building Management System.” Practical Application: This research proposed a machine learning model of indoor airflow characteristics of a classroom in Yogyakarta. The proposed model can be adapted to produce monitoring systems that best represent the related conditions. The method can be adopted to develop a relatively simple, low-cost sensor or model to monitor an indoor environment. Future studies may explore the results of the real-world implementation in a case study. © The Author(s) 2024.
Item Type: | Article |
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Additional Information: | Cited by: 2 |
Uncontrolled Keywords: | Air conditioning; Air quality; Climate models; COVID-19; Energy efficiency; Indoor air pollution; Intelligent buildings; Mean square error; Tropics; Ventilation; Air flow modeling; Airflow distribution; Environmental monitoring system; Healthy buildings; Indoor air quality; Indoor airflow; Indoor airflow distribution; Indoor environmental quality; Machine-learning; Real- time; Machine learning |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Divisions: | Faculty of Engineering > Nuclear and Physics Engineering Department |
Depositing User: | Rita Yulianti Yulianti |
Date Deposited: | 13 Mar 2025 01:06 |
Last Modified: | 13 Mar 2025 01:06 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/13248 |