BUILDA: A Thermal Building Data Generation Framework for Transfer Learning
This work addresses a data scarcity problem for researchers in building thermal dynamics, though it is incremental as it builds on existing simulation methods.
The paper tackles the lack of sufficient thermal building data for transfer learning research by introducing BuilDa, a framework that generates synthetic data without requiring expert simulation knowledge, and demonstrates its use in pretraining and fine-tuning models.
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.