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 language | English (US) |
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Pages (from-to) | 439-444 |
Number of pages | 6 |
Journal | Energy Procedia |
Volume | 122 |
DOIs | |
State | Published - 2017 |
Event | International Conference on Future Buildings and Districts - Energy Efficiency from Nano to Urban Scale, CISBAT 2017 - Lausanne, Switzerland Duration: Sep 6 2017 → Sep 8 2017 |
All Science Journal Classification (ASJC) codes
- General Energy
Keywords
- Benchmark Data Set
- Big Data
- Machine Learning
- Non-Residential Building Meter Data
- Open Data