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The HVAC (Heating, Ventilation, and Air Conditioning) system is an important component of a building's energy consumption, and its primary function is to provide a comfortable thermal environment for occupants. Accurate prediction of occupant thermal comfort is essential for improving building energy utilization as well as health and work efficiency. Therefore, the development of accurate thermal comfort prediction models is of great value. Deep learning based on data-driven techniques has excellent potential for predicting thermal comfort due to the development of artificial intelligence. However, the inability to obtain large quantities of detailed thermal comfort labeling data from residents presents a substantial challenge to the modeling endeavor. This paper proposes a building-to-building transfer learning framework to make deep learning models applicable in data-limited interior building environments, thereby resolving the issue and enhancing model predictive performance. The transfer learning method (TL) is applied to a novel technology dubbed the Transformer model, which has demonstrated outstanding performance in data trend prediction. The model exploits the spatiotemporal relationship of data regarding thermal comfort. Experiments are conducted using the source dataset (Scales project dataset and ASHRAE RP-884 dataset) and the target dataset (Medium US office dataset), and the results show that the proposed TL-Transformer achieves 62.6% accuracy, 57% precision, and a 59% F1 score, and the prediction performance is better than other existing methods. The model is useful for predicting indoor thermal comfort in buildings with limited data, and its validity is verified by experimental results. [ABSTRACT FROM AUTHOR]