通过可定制激励策略生成建筑热动态数据，提升模型泛化能力
BuilDyn: Excitation-Driven Data Generation for Building Thermal Dynamics Modeling and Control
- 基于BuilDa构建，支持自定义控制激励策略
- 在单栋建筑上训练的模型性能提升显著
- 适合做建筑能效控制与迁移学习的研究者
机器学习正广泛用于建筑数据驱动建模，以支持故障检测与诊断、节能控制等下游任务。尽管现有方法提升了跨建筑特征、天气和使用情况的泛化能力，但泛化效果仍依赖于控制驱动状态空间的充分探索。当前真实数据集和仿真环境大多反映固定控制策略下的稳态运行，导致激励不足，模型对未见工况鲁棒性差。本文提出BuilDyn，一个基于BuilDa的工具包，支持面向控制的数据生成中可定制的激励策略，并能从代表性建筑分布中采样，提供Python接口便于集成到机器学习流程中。通过对比在非激励与激励数据上训练的模型性能，验证了BuilDyn的有效性。我们期望该工具推动可扩展的控制导向建模，支持迁移学习与建筑专用基础模型等未来方向。
原文摘要 · Abstract (English)
Machine learning (ML) is increasingly used for data-driven modeling of buildings to enable downstream tasks such as fault detection and diagnosis, and energy-efficient control. While recent work improves generalization across building characteristics, weather, and occupancy, generalization also depends on sufficient exploration of the control-driven system state space. Existing real-world datasets and simulation environments predominantly reflect stationary operation under fixed control policies, resulting in limited excitation and reduced robustness to unseen operating conditions. This paper introduces BuilDyn, a package based on BuilDa that enables customizable excitation strategies for control-oriented data generation. BuilDyn further supports sampling from representative building distributions and provides a Python interface for easy integration into machine learning pipelines. We demonstrate the benefits of BuilDyn by comparing the performance of data-driven ML models trained on non-excited and excited data for one building. With BuilDyn, we hope to advance scalable control-oriented modeling and support future directions such as transfer learning and building-specific foundation models.
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