Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

9-2017

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.

Keywords

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

Discipline

Energy Policy | Engineering

Research Areas

Integrative Research Areas

Publication

CISBAT 2017 International Conference Future Buildings & Districts Energy Efficiency from Nano to Urban Scale, Lausanne, Switzerland, September 6-8

Volume

122

First Page

439

Last Page

444

Identifier

10.1016/j.egypro.2017.07.400

Publisher

Elsevier

City or Country

Lausanne, Switzerland

Additional URL

https://doi.org/10.1016/j.egypro.2017.07.400

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