构建可配置的建筑热力数据生成框架，助力机器学习研究
A Highly Configurable Framework for Large-Scale Thermal Building Data Generation to drive Machine Learning Research
- 基于Modelica模型与FMU接口，在Python中实现自动化仿真生成
- 生成486个数据驱动模型用于迁移学习，验证数据有效性
- 无需建筑模拟专业知识，适合大规模数据需求场景
基于机器学习的数据驱动建筑热力建模正成为大规模智能建筑控制的重要研究方向。然而，此类研究需要海量高质量的建筑热力数据，而现有公开数据集和数据生成工具在数据量与质量上均难以满足需求。此外，现有生成方法通常依赖建筑模拟专业经验。为此，本文提出名为BuilDa的热力建筑数据生成框架，可高效生成满足机器学习研究所需的合成数据。该框架采用单区Modelica模型，导出为功能性仿真单元（FMU），并在Python中进行仿真。我们通过生成数据并用于486个数据驱动模型的迁移学习微调实验，验证了框架的有效性。
原文摘要 · Abstract (English)
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.
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