Elmaz, FurkanFurkanElmazGhane, SaraSaraGhaneHuybrechts, ThomasThomasHuybrechtsAnwar, AliAliAnwarMercelis, SiegfriedSiegfriedMercelisHellinckx, PeterPeterHellinckx2026-03-242026-03-2420221553-572Xhttps://imec-publications.be/handle/20.500.12860/58940Indoor temperature modeling has been a vital component to develop accurate digital twins and smart controllers for buildings. Hybrid (also known as gray-box) modeling caught significant attention from the literature for this task. Combining the accumulated physical knowledge we have about thermal behavior with modern data-driven techniques promises more accurate and stable prediction models which can be used in various applications. However, methods such as data-driven parameter optimization and constrained training proposed in the literature show practical limitations such as high computational expense and software incompatibilities. In this paper we propose a transfer learning-based hybrid modeling approach where a CNN-LSTM model is pre-trained with the simulation data and then refined with the real-life data, thus, creating a completely data-driven hybrid model. We compared our approach to the same CNN-LSTM architecture trained only on real-life data. We reported significant accuracy and stability increases with the proposed approach.engProceedings paper10.1109/IECON49645.2022.9968939WOS:001504976200616