# GenTL: A General Transfer Learning Model for Building Thermal Dynamics

arXiv: https://arxiv.org/abs/2501.13703 · HTML: https://mlsanity.com/paper/2501.13703/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 41 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Generalized Online Transfer Learning for Climate Control in Residential Buildings](https://mlsanity.com/paper/1610.04042/) (2016)
- [Deep Transfer Learning for Thermal Dynamics Modeling in Smart Buildings](https://mlsanity.com/paper/1911.03318/) (2019)

## Built on by (most cited first, 10 of 10)

- [Adapting to Change: A Comparison of Continual and Transfer Learning for Modeling Building Thermal Dynamics under Concept Drifts](https://mlsanity.com/paper/2508.21615/) (2025)
- [BUILDA: A Thermal Building Data Generation Framework for Transfer Learning](https://mlsanity.com/paper/2508.12703/) (2025)
- [Thermal-GEMs: Generalized Models for Building Thermal Dynamics](https://mlsanity.com/paper/2604.16443/) (2026)
- [Counter-Dyna: Data-Efficient RL-Based HVAC Control using Counterfactual Building Models](https://mlsanity.com/paper/2605.04555/) (2026)
- [Neural Parameter Estimation of RC Thermal Building Models for Model Predictive Control](https://mlsanity.com/paper/2604.05904/) (2026)
- [BuilDyn: Excitation-Driven Data Generation for Building Thermal Dynamics Modeling and Control](https://mlsanity.com/paper/2605.29849/) (2026)
- [CaberNet: Causal Representation Learning for Cross-Domain HVAC Energy Prediction](https://mlsanity.com/paper/2511.06634/) (2025)
- [RESCAST-100K: A Comprehensive Dataset for Cross-Domain Residential Load and Indoor Temperature Forecasting](https://mlsanity.com/paper/2606.02852/) (2026)
- [Very Exciting: Zero-Shot Model Predictive Control of Buildings via Excitation-Based Generalized Transfer Learning Models](https://mlsanity.com/paper/2609.12853/) (2026)
- [The Smart Buildings Challenge: Learning Foundation Models for Real-World Optimization at Scale](https://mlsanity.com/paper/2610.18511/) (2026)

## Abstract (arXiv)

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.