eess.SYApr 7, 2026

Thermal-GEMs: Generalized Models for Building Thermal Dynamics

Authors: Felix Koch, Fabian Raisch, Benjamin Tischler

Organizations: Technical University of Applied Sciences Rosenheim, Rosenheim, Germany · Technical University of Munich, Munich, Germany

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Abstract

Data-driven models for building thermal dynamics are a scalable approach for enabling energy-efficient operation through fault detection & diagnosis or advanced control. To obtain accurate models, measurement data from a target building spanning months to years are required. Transfer Learning (TL) mitigates this challenge by employing pretrained models based on single or multiple source buildings. General multi-source TL models promise to outperform single-source TL, but alternative multi-source modeling architectures remain to be explored, and evaluation on real-world data is missing. Moreover, time series foundation models (TSFM) have emerged as candidates for the best-performing general models. Hence, we conduct a first, comprehensive assessment of general modeling approaches for building thermal dynamics, including multi-source TL and TSFMs. Our assessment includes ablations using four state-of-the-art multi-source TL architectures and evaluations on synthetic as well as real-world data. We demonstrate that multi-source TL models are highly effective in accurately modeling buildings in real-world applications, yielding up to 63% lower forecasting errors compared to single-source TL. Moreover, our results suggest a trade-off between multi-source TL models exclusively pretrained with building data and TSFMs pretrained with a multitude of different time series, revealing that data from 16-32 source buildings must be available over 1 year for pretraining multi-source TL models to consistently outperform TSFMs as evaluated using the mean absolute error. These findings provide practical guidance for selecting modeling strategies based on the number of source buildings available for pretraining multi-source TL models.

Figures & tables

Figure 1. Overview of modeling strategies regarding our research questions.
ObservableDataset
NameTypeD1D2D3
Indoor HumidityFeature✓×\times×\times
Thermostat Heat SetpointFeature✓×\times✓
Thermostat Cool SetpointFeature✓×\times×\times
Supply Air Fan RuntimeFeature✓×\times×\times
Presence of OccupantsFeature✓×\times×\times
Table 1. Features included in the three datasets.
 EcoBeeIdealEuro. House
# Rooms Total932227450
# Sources Total892202400
# Targets402550
% Targets4.311.011.1
Table 2. Source and target split for all datasets
Figure 2. Seasonal Fine-Tuning split for the experiments
Figure 3. Zero-Shot MAE of all architectures as MSGMs
Figure 4. MAE of Single-Source TL versus MSGM for different fine-tunings on real-world data (Ecobee). MSGM beats single-source TL once errors are above the dotted line.
Figure 5. Zero-shot performance: TSFMs vs. the best-performing MSGMs (Transformer for Euro, LSTM for Ecobee and Ideal).
Figure 6. Zero-shot performance: Per-building TSFM compared to MSGM (each data point is a target building)
Figure 7. Zero-shot performance: Influence of the number of sources on the MSGM for buildings. The best-performing TSFM with respect to each dataset is used as a baseline.
Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

HyperparameterLSTMTransformerMambaxLSTM
FromToStepFromToStepFromToStepFromToStep
Hidden Size6425632642563264256326425632
# Layers241241281241
Batch Size3212832321283232128323212832
Learning Rate.0001.001.0001.0001.001.0001.0001.001.0001.0001.001.0001
Dropout0.00.10.010.00.10.01---0.00.10.01
Appendix
Table 3. Hyperparameter Search Space for the Multi-Source General Models.
ParameterLSTMTransformerMambaxLSTM
EcobeeIdealEuroEcobeeIdealEuroEcobeeIdealEuroEcobeeIdealEuro
Hidden Size96128192192128192160128969612864
# Layers333433343333
Batch Size1281289612812812812812896128128128
Use AMPTrueTrueTrueTrueTrueTrueTrueTrueTrueFalseFalseFalse
Learning Rate.0009.001.001.0001.0005.0004.0006.0002.0002.0002.0005.0005
Appendix
Table 4. Hyperparameters for all MSGM architectures for each dataset.
Figure 8. RMSE of Single-Source TL versus MSGM for different fine-tunings on real-world data (Ecobee). MSGM beats single-source TL once errors are above the dotted line.
Figure 9. Zero-shot performance RMSE: Influence of the number of sources on the MSGM for buildings. The best-performing TSFM with respect to each dataset is used as a baseline.
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