Enhancing indoor thermal comfort prediction in tropical regions: A transfer learning strategy in West Bengal

Ensuring energy-efficient building management and sustainability necessitates precise thermal comfort prediction, a task particularly critical in tropical regions with significant energy-saving potential. This study confronts the complexities introduced by humid air conditions and escalating internal and external heat levels. The objective is to devise a sophisticated strategy for predicting indoor personal thermal comfort that enhances prediction accuracy. The proposed method extends beyond previous techniques by incorporating a comprehensive array of thermal comfort factors and their potential interdependencies, while also leveraging a transductive transfer learning strategy to circumvent data sparsity in the target domain. The experimental results indicate the presence of localized feature coupling within thermal comfort datasets, which the proposed CNN-ConvTrans model effectively processes, thereby enhancing predictive efficacy. The primary innovation of this work lies in the development of a novel transfer learning strategy that considers localized feature coupling, integrated with a deep learning model, effectively addressing issues such as class imbalance and data scarcity. This study furnishes an innovative and valuable framework for thermal comfort prediction across diverse environmental settings.