BTS: Building Timeseries Dataset: Empowering Large-Scale Building Analytics
This dataset addresses a bottleneck for researchers in building analytics by providing standardized data to improve interoperability and performance optimization, though it is incremental as it builds on existing data collection efforts.
The paper tackles the lack of accessible real-world datasets for building analytics by introducing the BTS dataset, which includes over ten thousand timeseries data points from three buildings over three years, and demonstrates its utility through benchmarks on ontology classification and zero-shot forecasting tasks.
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 .