Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.egypro.2017.07.400
Title: The Building Data Genome Project: An open, public data set from non-residential building electrical meters
Authors: Miller, Clayton 
Meggers, Forrest 
Keywords: Open Data
Non-Residential Building Meter Data
Benchmark Data Set
Big Data
Machine Learning
Issue Date: 1-Jan-2017
Publisher: ELSEVIER SCIENCE BV
Citation: Miller, Clayton, Meggers, Forrest (2017-01-01). The Building Data Genome Project: An open, public data set from non-residential building electrical meters. 7th International Conference on Future Buildings and Districts - Energy Efficiency from Nano to Urban Scale (CISBAT) 122 : 439-444. ScholarBank@NUS Repository. https://doi.org/10.1016/j.egypro.2017.07.400
Abstract: 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.
Source Title: 7th International Conference on Future Buildings and Districts - Energy Efficiency from Nano to Urban Scale (CISBAT)
URI: https://scholarbank.nus.edu.sg/handle/10635/189464
ISSN: 18766102
DOI: 10.1016/j.egypro.2017.07.400
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