BUILDA: A Thermal Building Data Generation Framework for Transfer Learning

• URL: http://arxiv.org/abs/2508.12703v1
• Date: Mon, 18 Aug 2025 08:01:37 GMT
• Title: BUILDA: A Thermal Building Data Generation Framework for Transfer Learning
• Authors: Thomas Krug, Fabian Raisch, Dominik Aimer, Markus Wirnsberger, Ferdinand Sigg, Benjamin Schäfer, Benjamin Tischler,
• Abstract summary: Transfer learning can improve data-driven modeling of building thermal dynamics.<n>We present BuilDa, a framework for producing synthetic data of adequate quality and quantity for TL research.
• Score: 26.47874938214435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transfer learning (TL) can improve data-driven modeling of building thermal dynamics. Therefore, many new TL research areas emerge in the field, such as selecting the right source model for TL. However, these research directions require massive amounts of thermal building data which is lacking presently. Neither public datasets nor existing data generators meet the needs of TL research in terms of data quality and quantity. Moreover, existing data generation approaches typically require expert knowledge in building simulation. We present BuilDa, a thermal building data generation framework for producing synthetic data of adequate quality and quantity for TL research. The framework does not require profound building simulation knowledge to generate large volumes of data. BuilDa uses a single-zone Modelica model that is exported as a Functional Mock-up Unit (FMU) and simulated in Python. We demonstrate BuilDa by generating data and utilizing it for pretraining and fine-tuning TL models.

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