arXiv 建筑语义标准论文· Arian Prabowo·· 2024-06-13AI 评分61
「英中对照」BTS（Building TimeSeries）：建筑时序数据集 赋能大规模建筑分析
BTS: Building Timeseries Dataset: Empowering Large-Scale Building Analytics
AI 导读
面向大规模建筑分析的Building TimeSeries（BTS）建筑时序数据集已随arXiv建筑语义标准论文公开。该数据集覆盖3栋建筑为期3年的数据，包含超1万个时序数据点、数百种独特本体，元数据采用Brick schema标准化。研究人员已针对时序本体分类、零样本预测两项任务开展基准测试，数据集与基准测试代码可通过对应链接获取。
正文 · 原文
Abstract: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: this https URL .
| Comments: | 21 pages, 2 figures, 9 tables, under review |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2406.08990 [cs.LG] |
| (or arXiv:2406.08990v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2406.08990 arXiv-issued DOI via DataCite |
Submission history
From: Arian Prabowo [view email]
[v1]
Thu, 13 Jun 2024 10:38:38 UTC (1,500 KB)
[v2]
Tue, 18 Jun 2024 17:54:13 UTC (2,659 KB)
来源：arXiv 建筑语义标准论文 · arxiv.org