A stacked ensemble learning framework with logistic regression meta-learner for thermal comfort prediction

Abstract

Thermal comfort is a critical determinant of human health, well-being and productivity, and is also integral to promoting energy efficiency. The predicted mean vote is the most recognized method for estimating the average thermal experience amongst a group of individuals within built environments. However, the method's reliance on climatic parameters that are difficult and resource-intensive to measure, as well as physiological parameters that require self-reporting, introduces significant practical limitations for real-world applications. The present work aims to address these limitations by proposing a lightweight predictive framework for effective, streamlined thermal comfort classification that relies on a reduced input feature space comprising the easy-to-measure and low-cost climatic parameters of air temperature and relative humidity, and the seasonal standardized approximation for the physiological parameter of clothing insulation. Leveraging an ensemble learning architecture with random forest, k-nearest neighbours, CatBoost and multi-layer perceptron as weak learners and logistic regression as a meta-learner, the proposed framework demonstrated an overall predictive accuracy of 85.8% in estimating the average thermal experience. It adequately handled the class imbalance across thermal discomfort states, particularly those underrepresented, further underscoring its robust performance. The proposed framework could emerge as a scalable and efficient approach for estimating thermal comfort in real-world applications.