用时序基础模型提升建筑能耗预测，解决数据异构难题。
Exploring Capabilities of Time Series Foundation Models in Building Analytics
- 使用两个公开物联网数据集评估时序基础模型性能
- 单模态模型在应对数据多样性和物理限制上表现优异
- 适合关注建筑能效优化的研究者与从业者
数字化基础设施与物联网（IoT）网络的融合，正推动建筑能源管理与优化的转型。借助基于IoT的监控系统，建筑管理者、能源供应商及政策制定者可基于数据做出更明智的决策，以提升能源效率。然而，精准的能源预测与分析仍面临挑战，主要源于建筑固有的物理约束以及物联网生成数据的多样性和异质性。本研究对两个公开的IoT数据集进行了全面基准测试，评估了时序基础模型在建筑能源分析中的表现。结果表明，单模态模型在克服数据变异性与建筑物理限制方面展现出显著潜力，未来工作将聚焦于优化多模态模型，以支持可持续能源管理。
原文摘要 · Abstract (English)
The growing integration of digitized infrastructure with Internet of Things (IoT) networks has transformed the management and optimization of building energy consumption. By leveraging IoT-based monitoring systems, stakeholders such as building managers, energy suppliers, and policymakers can make data-driven decisions to improve energy efficiency. However, accurate energy forecasting and analytics face persistent challenges, primarily due to the inherent physical constraints of buildings and the diverse, heterogeneous nature of IoT-generated data. In this study, we conduct a comprehensive benchmarking of two publicly available IoT datasets, evaluating the performance of time series foundation models in the context of building energy analytics. Our analysis shows that single-modal models demonstrate significant promise in overcoming the complexities of data variability and physical limitations in buildings, with future work focusing on optimizing multi-modal models for sustainable energy management.
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