Buildings play a crucial role in human well-being, influencing occupant comfort, health, and safety. Additionally, they contribute significantly to global energy consumption, accounting for one-third of total energy usage, and carbon emissions. Optimizing building performance presents a vital opportunity to combat climate change and promote human flourishing. However, research in building analytics has been hampered by the lack of accessible, available, and comprehensive real-world datasets on multiple building operations. In this paper, we introduce the Building TimeSeries (BTS) dataset. Our dataset covers three buildings over a three-year period, comprising more than ten thousand timeseries data points with hundreds of unique ontologies. Moreover, the metadata is standardized using the Brick schema. To demonstrate the utility of this dataset, we performed benchmarks on two tasks: timeseries ontology classification and zero-shot forecasting. These tasks represent an essential initial step in addressing challenges related to interoperability in building analytics. Access to the dataset and the code used for benchmarking are available here: https://github.com/cruiseresearchgroup/DIEF_BTS .

翻译：建筑在人类福祉中扮演着关键角色，影响着居住者的舒适度、健康与安全。此外，建筑在全球能源消耗和碳排放中占显著比重，占总能耗的三分之一。优化建筑性能为应对气候变化和促进人类繁荣提供了重要机遇。然而，建筑分析领域的研究一直受限于缺乏可获取、可用且全面的多建筑运行真实世界数据集。本文介绍了建筑时序（BTS）数据集。我们的数据集涵盖三栋建筑为期三年的运行数据，包含超过一万个时序数据点，涉及数百种独特的本体。此外，元数据采用Brick模式进行了标准化处理。为展示该数据集的实用性，我们在两个任务上进行了基准测试：时序本体分类和零样本预测。这些任务是解决建筑分析中互操作性相关挑战的关键初步步骤。数据集及基准测试所用代码可通过以下链接获取：https://github.com/cruiseresearchgroup/DIEF_BTS 。