@article{35306,
  author = {Na Luo and Zhe Wang and David Blum and Christopher Weyandt and Norman Bourassa and Mary Ann Piette and Tianzhen Hong},
  title = {A three-year dataset supporting research on building energy management and occupancy analytics},
  abstract = {<p><span>This paper presents the curation of a monitored dataset from an office building constructed in 2015 in Berkeley, California. The dataset includes whole-building and end-use energy consumption, HVAC system operating conditions, indoor and outdoor environmental parameters, as well as occupant counts. The data were collected during a period of three years from more than 300 sensors and meters on two office floors (each 2,325 m</span><span>2</span><span>) of the building. A three-step data curation strategy is applied to transform the raw data into research-grade data: (1) cleaning the raw data to detect and adjust the outlier values and fill the data gaps; (2) creating the metadata model of the building systems and data points using the Brick schema; and (3) representing the metadata of the dataset using a semantic JSON schema. This dataset can be used in various applications{\textemdash}building energy benchmarking, load shape analysis, energy prediction, occupancy prediction and analytics, and HVAC controls{\textemdash}to improve the understanding and efficiency of building operations for reducing energy use, energy costs, and carbon emissions.</span></p>},
  year = {2022},
  booktitle = {Nature Scientific Data},
  journal = {Nature Scientific Data},
  series = {Nature Scientific Data},
  number = {156},
  month = {04/2022},
  url = {https://www.nature.com/articles/s41597-022-01257-x},
  doi = {https://doi.org/10.1038/s41597-022-01257-x},
  language = {eng},
}