  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 .
