Thermal and visual comforts of occupants for a naturally ventilated educational building in low-income economies: A machine learning approach

Thermal and visual comfort (TVC) significantly impact the health and productivity of occupants as well as the indoor environmental quality in educational buildings. Machine learning approaches have currently gained popularity for TVC prediction; however, accurately predicting TVC faces challenges due to instrumental errors, environmental noise, and imbalanced data. Moreover, studies have revealed the unreliability of TVC predictions, particularly in naturally ventilated buildings. Therefore, this study develops three machine learning models, random forest (RF), decision tree (DT), and XGBoost, to accurately predict TVC using 1310 sets of field survey data collected from a naturally ventilated educational building (NVEB) in Dhaka, Bangladesh. Recursive feature elimination with cross-validation is employed to identify optimal features, while two resampling methods are applied to manage imbalanced classification. The RF model achieves the highest accuracy of 96 % for thermal comfort prediction, while the XGBoost model attains the highest accuracy of 95 % for visual comfort prediction. Meanwhile, the DT model achieves an accuracy of 94 % and 93 % for TVC prediction, respectively. Moreover, both RF and XGBoost outperform DT in class prediction performance, with an average area under the curve (AUC) scores above 0.95, while DT achieves an average AUC score above 0.80. In terms of two resampling methods, the results show that the models developed using the synthetic minority over-sampling technique (SMOTE) + edited nearest neighbour approach leads to less misclassification compared to the SMOTE + Tomek link. These findings demonstrate the capability of machine learning models in accurately predicting TVC in NVEBs and handling imbalanced datasets.