BuilDa无需专业知识即可生成大量高质量建筑热力数据，支持迁移学习研究。
BUILDA: A Thermal Building Data Generation Framework for Transfer Learning
- 基于Modelica单区模型与FMU接口，在Python中自动化仿真生成数据。
- 可生成适用于预训练与微调的海量建筑热力数据，满足迁移学习需求。
- 降低专业门槛，适合缺乏建筑模拟经验的研究者使用。
迁移学习（TL）可提升建筑热动态数据驱动建模的性能，推动了诸多新研究方向，如源模型选择等。然而这些研究亟需大量热力建筑数据，而现有公开数据集和数据生成工具在数据质量与数量上均无法满足需求。此外，现有生成方法通常需要建筑仿真专业知识。本文提出BuilDa，一个面向迁移学习研究的建筑热力数据生成框架，可无需深入建筑模拟知识即可生成大规模、高质量的合成数据。该框架采用单区Modelica模型导出为功能性模拟单元（FMU），并在Python中进行仿真。我们通过生成数据并用于预训练和微调迁移学习模型，验证了BuilDa的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。