Data-driven modeling of building thermal dynamics is emerging as an increasingly important field of research for large-scale intelligent building control. However, research in data-driven modeling using machine learning (ML) techniques requires massive amounts of thermal building data, which is not easily available. Neither empirical public datasets nor existing data generators meet the needs of ML research in terms of data quality and quantity. Moreover, existing data generation approaches typically require expert knowledge in building simulation. To fill this gap, we present a thermal building data generation framework which we call BuilDa. BuilDa is designed to produce synthetic data of adequate quality and quantity for ML 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 a transfer learning study involving the fine-tuning of 486 data-driven models.

翻译：建筑热动力学的数据驱动建模正日益成为大规模智能建筑控制研究的重要领域。然而，基于机器学习（ML）技术的数据驱动建模研究需要大量建筑热数据，而这些数据不易获取。现有的公开实证数据集及数据生成工具均无法满足机器学习研究在数据质量和数量方面的需求。此外，现有数据生成方法通常需要具备建筑模拟领域的专业知识。为填补这一空白，我们提出了一个名为BuilDa的建筑热数据生成框架。该框架旨在为机器学习研究生成具备足够质量与规模的合成数据，且无需深厚的建筑模拟知识即可生成海量数据。BuilDa采用单区域Modelica模型，将其导出为功能模拟单元（FMU）并在Python环境中进行仿真。我们通过生成数据并应用于涉及486个数据驱动模型微调的迁移学习研究，验证了BuilDa的实用性。