BTS: Building Timeseries Dataset: Empowering Large-Scale Building Analytics
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917698141683712 |
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| author | Prabowo, Arian Lin, Xiachong Razzak, Imran Xue, Hao Yap, Emily W. Amos, Matthew Salim, Flora D. |
| author_facet | Prabowo, Arian Lin, Xiachong Razzak, Imran Xue, Hao Yap, Emily W. Amos, Matthew Salim, Flora D. |
| contents | 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 . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_08990 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BTS: Building Timeseries Dataset: Empowering Large-Scale Building Analytics Prabowo, Arian Lin, Xiachong Razzak, Imran Xue, Hao Yap, Emily W. Amos, Matthew Salim, Flora D. Machine Learning 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 . |
| title | BTS: Building Timeseries Dataset: Empowering Large-Scale Building Analytics |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.08990 |