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GenTL: A General Transfer Learning Model for Building Thermal Dynamics

Primary research

#428

T1digested

Canonical URL: http://arxiv.org/abs/2501.13703v2
Topic: Industrial LLM Applications
First seen: 2026-07-17 07:16:08
Last seen: 2026-07-17 07:16:08

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arXiv2026-07-17 07:15:08
GenTL: A General Transfer Learning Model for Building Thermal Dynamics
Transfer Learning (TL) is an emerging field in modeling building thermal dynamics. This method reduces the data required for a data-driven model of a target building by leveraging knowledge from a source building. Consequently, it enables the creation of data-efficient models that can be used for advanced control and fault detection & diagnosis. A major limitation of the TL approach is its inconsistent performance across different sources. Although accurate source-building selection for a target is crucial, it remains a persistent challenge. We present GenTL, a general transfer learning model for single-family houses in Central Europe. GenTL can be efficiently fine-tuned to a large variety of target buildings. It is pretrained on a Long Short-Term Memory (LSTM) network with data from 450 different buildings. The general transfer learning model eliminates the need for source-building selection by serving as a universal source for fine-tuning. Comparative analysis with conventional single-source to single-target TL demonstrates the efficacy and reliability of the general pretraining approach. Testing GenTL on 144 target buildings for fine-tuning reveals an average prediction error (RMSE) reduction of 42.1 % compared to fine-tuning single-source models.