A three-year building operational performance dataset for informing energy efficiency

This dataset was curated from an office building constructed in 2015 in
Berkeley, California, which includes whole-building and end-use energy
consumption, HVAC system operating conditions, indoor and outdoor
environmental parameters, and occupant counts. The data was collected in
three years from more than 300 sensors and meters for two office floors
(each 2,325 m2) of the building. A three-step data curation strategy is
applied to transform the raw data into the 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; (3) describing the metadata of the
dataset using a semantic JSON schema. This dataset can be used for various
types of applications, including building energy benchmarking, load shape
analysis, energy prediction, occupancy prediction and analytics, and HVAC
controls to improve understanding and efficiency of building operations
for reducing energy use, energy costs, and carbon emissions.