Work

A dynamic adaptive multi-source aggregated transfer learning framework for individual thermal comfort prediction

Year: 2026

Type: article

Abstract: Occupants’ thermal comfort is critical for building operation and energy management. However, thermal comfort is influenced by multiple factors and changes dynamically, making accurate prediction challenging. As a result, existing methods face key challenges, including limited accuracy, inefficient generalization, and lack of dynamic adaptability. To address them, this study develops a dynamic adaptive multi-source information aggregated transfer learning framework. Specifically, a multi-source information aggregated MLPMixer model (TCMLPMixer) is developed to improve prediction accuracy under the same input modalities and a comparable model size by modeling feature interactions among diverse thermal comfort factors. In addition, a similarity-aware efficient transfer learning (TL) strategy is introduced to improve transfer efficiency under different data-availability conditions. Furthermore, a dynamic adaptive online learning procedure is developed to enable non-intrusive pseudo-label acquisition, and to support adaptive fine-tuning with different TL strategies according to data availability, thereby facilitating dynamically adaptive thermal comfort prediction. An orthogonal experimental design is employed to construct a multi-source individual thermal comfort dataset. Experimental results show that our framework consistently outperforms baselines. Specifically, under the same input modalities and a comparable model size, the pre-trained TCMLPMixer outperforms the DNN model by 7.40 percentage points, while under limited-data conditions in the target domain, it outperforms the DNN-based transfer model by 4.82 percentage points in accuracy. Moreover, it exhibits strong dynamic adaptability through progressive online updates, with target-domain accuracy increasing from 79.63% to 92.57% during long-term deployment. These results demonstrate that our framework can achieve improved prediction accuracy, enhanced generalization, and dynamic adaptability, demonstrating its potential applicability to occupant-centered intelligent building control.

Source: Applied Thermal Engineering

DOI: 10.1016/j.applthermaleng.2026.133350