BUILDA: A Thermal Building Data Generation Framework for Transfer Learning

TL;DR

BuilDa is a framework that generates high-quality synthetic thermal building data to support transfer learning research, eliminating the need for expert simulation knowledge and enabling large-scale data creation.

Contribution

It introduces a user-friendly data generation framework using Modelica models for transfer learning in building thermal dynamics.

Findings

Generated data improves transfer learning model performance.

Framework simplifies data creation without expert simulation knowledge.

Demonstrated effectiveness through pretraining and fine-tuning experiments.

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.