Affiliation:
1. Faculty of Environmental Engineering, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland
Abstract
This study explores the capability of machine learning techniques (MLTs) in predicting IAQ in apartments. Sensor data from kitchen air monitoring were used to determine the conditions in the living room. The analysis was based on several air parameters—temperature, relative humidity, CO2 concentration, and TVOC—recorded in five apartments. Multiple input–multiple output prediction models were built. Linear (multiple linear regression and multilayer perceptron (MLP)) and nonlinear (decision trees, random forest, k-nearest neighbors, and MLP) methods were investigated. Five-fold cross-validation was applied, where four apartments provided data for model training and the remaining one was the source of the test data. The models were compared using performance metrics (R2, MAPE, and RMSE). The naive approach was used as the benchmark. This study showed that linear MLTs performed best. In this case, the coefficients of determination were highest: R2 = 0.94 (T), R2 = 0.94 (RH), R2 = 0.63 (CO2), R2 = 0.84 (TVOC, based on the SGP30 sensor), and R2 = 0.92 (TVOC, based on the SGP30 sensor). The prediction of distinct indoor air parameters was not equally effective. Based on the lowest percentage error, best predictions were attained for indoor air temperature (MAPE = 1.57%), relative humidity (MAPE = 2.97%RH), and TVOC content (MAPE = 0.41%). Unfortunately, CO2 prediction was loaded with high error (MAPE = 20.83%). The approach was particularly effective in open-kitchen apartments, and they could be the target for its application. This research offers a method that could contribute to attaining effective IAQ control in apartments.
Reference49 articles.
1. (2024, May 12). World Air Quality Report. Available online: https://www.jagranjosh.com/general-knowledge/world-air-quality-report-2020-all-about-delhi-being-the-most-polluted-capital-of-the-world-1615966983-1.
2. United States Environmental Protection Agency (2024, May 12). Introduction to Indoor Air Quality, Available online: https://www.epa.gov/indoor-air-quality-iaq/introduction-indoor-air-quality.
3. An electronic nose based on carbon nanotube -titanium dioxide hybrid nanostructures for detection and discrimination of volatile organic compounds;Shooshtari;Sens. Actuators B Chem.,2022
4. Peters, T., and Zheng, C. (2024). Evaluating Indoor Air Quality Monitoring Devices for Healthy Homes, Buildings. Buildings, 14.
5. Yasin, A., Delaney, J., Cheng, C.-T., and Pang, T.Y. (2022). The Design and Implementation of an IoT Sensor-Based Indoor Air Quality Monitoring System Using Off-the-Shelf Devices. Appl. Sci., 12.