Summary of Bts: Building Timeseries Dataset: Empowering Large-scale Building Analytics, by Arian Prabowo et al.
BTS: Building Timeseries Dataset: Empowering Large-Scale Building Analytics
by Arian Prabowo, Xiachong Lin, Imran Razzak, Hao Xue, Emily W. Yap, Matthew Amos, Flora D. Salim
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces the Building TimeSeries (BTS) dataset, a comprehensive real-world dataset on multiple building operations, covering three buildings over a three-year period. The dataset comprises more than ten thousand timeseries data points with hundreds of unique ontologies, standardized using the Brick schema. To demonstrate its utility, benchmarks are performed 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making buildings better for people and the planet. Right now, buildings use a lot of energy and contribute to climate change. To make things better, researchers need access to lots of data on how buildings work. This paper shares a new dataset that helps with this problem. The dataset has information from three different buildings over three years, with many different types of data points. It’s like a big puzzle with many pieces! The authors also did some tests to show what you can do with this data. They want to help make buildings better and more sustainable. |
Keywords
» Artificial intelligence » Classification » Zero shot
