TY - JOUR
T1 - The Building Data Genome Project 2, energy meter data from the ASHRAE Great Energy Predictor III competition
AU - Miller, Clayton
AU - Kathirgamanathan, Anjukan
AU - Picchetti, Bianca
AU - Arjunan, Pandarasamy
AU - Park, June Young
AU - Nagy, Zoltan
AU - Raftery, Paul
AU - Hobson, Brodie W.
AU - Shi, Zixiao
AU - Meggers, Forrest
N1 - Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12/1
Y1 - 2020/12/1
N2 - This paper describes an open data set of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 measurements per meter resulting in approximately 53.6 million measurements). These meters were collected from 19 sites across North America and Europe, with one or more meters per building measuring whole building electrical, heating and cooling water, steam, and solar energy as well as water and irrigation meters. Part of these data was used in the Great Energy Predictor III (GEPIII) competition hosted by the American Society of Heating, Refrigeration, and Air-Conditioning Engineers (ASHRAE) in October-December 2019. GEPIII was a machine learning competition for long-term prediction with an application to measurement and verification. This paper describes the process of data collection, cleaning, and convergence of time-series meter data, the meta-data about the buildings, and complementary weather data. This data set can be used for further prediction benchmarking and prototyping as well as anomaly detection, energy analysis, and building type classification.
AB - This paper describes an open data set of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 measurements per meter resulting in approximately 53.6 million measurements). These meters were collected from 19 sites across North America and Europe, with one or more meters per building measuring whole building electrical, heating and cooling water, steam, and solar energy as well as water and irrigation meters. Part of these data was used in the Great Energy Predictor III (GEPIII) competition hosted by the American Society of Heating, Refrigeration, and Air-Conditioning Engineers (ASHRAE) in October-December 2019. GEPIII was a machine learning competition for long-term prediction with an application to measurement and verification. This paper describes the process of data collection, cleaning, and convergence of time-series meter data, the meta-data about the buildings, and complementary weather data. This data set can be used for further prediction benchmarking and prototyping as well as anomaly detection, energy analysis, and building type classification.
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U2 - 10.1038/s41597-020-00712-x
DO - 10.1038/s41597-020-00712-x
M3 - Article
C2 - 33110076
AN - SCOPUS:85094119884
SN - 2052-4463
VL - 7
JO - Scientific Data
JF - Scientific Data
IS - 1
M1 - 368
ER -