The Building Data Genome Project: An open, public data set from non-residential building electrical meters

Clayton Miller, Forrest Meggers

Research output: Contribution to journalConference articlepeer-review

47 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)439-444
Number of pages6
JournalEnergy Procedia
Volume122
DOIs
StatePublished - 2017
EventInternational Conference on Future Buildings and Districts - Energy Efficiency from Nano to Urban Scale, CISBAT 2017 - Lausanne, Switzerland
Duration: Sep 6 2017Sep 8 2017

All Science Journal Classification (ASJC) codes

  • Energy(all)

Keywords

  • Benchmark Data Set
  • Big Data
  • Machine Learning
  • Non-Residential Building Meter Data
  • Open Data

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