Data-efficient Personalized Thermal Preference Models Using a Logistic Regression-Based Method

With the advancement of occupant-centric control research, adjusting indoor environmental parameters based on individual thermal preferences has become increasingly important. However, the diversity of individual thermal responses and complex interactions among influencing variables make accurate modelling challenging. To address these issues, this study proposes a data-efficient logistic regression model designed to overcome the limitations of conventional data-driven approaches that rely on large and balanced datasets. In this context, 'data-efficiency' refers to the model's ability to maintain reliable predictive performance even when trained with a limited number of data samples. The proposed model achieved accuracies of 64.0% and 80.4% when trained on 10 and 80 data points, respectively. The proposed approach offers a practical and interpretable framework for occupant-centric indoor environmental control systems, reducing data collection requirements while maintaining prediction reliability.