TY - JOUR
T1 - The Building Data Genome Project
T2 - International Conference on Future Buildings and Districts - Energy Efficiency from Nano to Urban Scale, CISBAT 2017
AU - Miller, Clayton
AU - Meggers, Forrest
N1 - Funding Information:
The authors would like to acknowledge the various campus facilities operations teams involved in the collection and dissemination of these data. This research was funded through an Institute of Technology in Architecture (ITA) Fellowship from the ETH Zürich.
Publisher Copyright:
© 2017 The Authors. Published by Elsevier Ltd.
PY - 2017
Y1 - 2017
N2 - As of 2015, there are over 60 million smart meters installed in the United States; these meters are at the forefront of big data analytics in the building industry. However, only a few public data sources of hourly non-residential meter data exist for the purpose of testing algorithms. This paper describes the collection, cleaning, and compilation of several such data sets found publicly on-line, in addition to several collected by the authors. There are 507 whole building electrical meters in this collection, and a majority are from buildings on university campuses. This group serves as a primary repository of open, non-residential data sources that can be built upon by other researchers. An overview of the data sources, subset selection criteria, and details of access to the repository are included. Future uses include the application of new, proposed prediction and classification models to compare performance to previously generated techniques.
AB - As of 2015, there are over 60 million smart meters installed in the United States; these meters are at the forefront of big data analytics in the building industry. However, only a few public data sources of hourly non-residential meter data exist for the purpose of testing algorithms. This paper describes the collection, cleaning, and compilation of several such data sets found publicly on-line, in addition to several collected by the authors. There are 507 whole building electrical meters in this collection, and a majority are from buildings on university campuses. This group serves as a primary repository of open, non-residential data sources that can be built upon by other researchers. An overview of the data sources, subset selection criteria, and details of access to the repository are included. Future uses include the application of new, proposed prediction and classification models to compare performance to previously generated techniques.
KW - Benchmark Data Set
KW - Big Data
KW - Machine Learning
KW - Non-Residential Building Meter Data
KW - Open Data
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U2 - 10.1016/j.egypro.2017.07.400
DO - 10.1016/j.egypro.2017.07.400
M3 - Conference article
AN - SCOPUS:85029903872
SN - 1876-6102
VL - 122
SP - 439
EP - 444
JO - Energy Procedia
JF - Energy Procedia
Y2 - 6 September 2017 through 8 September 2017
ER -