Plant Impact on Indoor Carbon Dioxide Concentration Using Ensemble Voting Prediction Models
Journal
Journal of Architectural/Planning Research and Studies
Journal Volume
23
Journal Issue
1
Start Page
275597
ISSN
27738868
Date Issued
2026
Author(s)
Rinchumphu, Damrongsak
Buachart, Chinnapat
Timprae, Warut
Manokeaw, Sattaya
Khuwuthyakorn, Pattaraporn
Kongwee, Worawut
Abstract
Indoor air quality, particularly carbon dioxide (CO₂) levels, is critical to occupants’ health and comfort. This study developed predictive models for indoor CO₂ concentrations based on environmental variables, including light, temperature, humidity, and the presence of plants. Data collected from sensors within a controlled indoor environment were used to train predictive models using various techniques, including Artificial Neural Networks (ANN), k-Nearest Neighbors (k-NN), Random Forest, and Generalized Linear Models. Among standalone models, the ANN with a 70:30 train-test split yielded the best performance, achieving a root mean square error (RMSE) of 10.960, mean absolute error (MAE) of 7.300, and a coefficient of determination (R²) of 0.640. The study further explored ensemble methods by combining ANN, k-NN, and Generalized Linear Models through soft voting. The optimal ensemble configuration—ANN and k-NN with a 90:10 split ratio—achieved an RMSE of 11.437, MAE of 8.153, and R² of 0.650, outperforming the standalone models. In addition, the results demonstrated that the presence of plants within a room reduced CO₂ levels under specific conditions (20-30°C and 200 lux), highlighting plants’ potential to improve indoor air quality. This research suggests that ensemble models offer a viable solution for accurate indoor CO₂ prediction, with practical applications in indoor environmental management, especially when coupled with biophilic design elements such as indoor plants.
Subjects
Carbon dioxide
Indoor air quality
Machine learning
Plants
Soft voting classifier
Publisher
Faculty of Architecture and Planning Thammasat University
Type
journal article