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NILMTK v0.2: A non-intrusive load monitoring toolkit for large scale data sets

  • Jack Kelly
  • , Nipun Batra
  • , Oliver Parson
  • , Haimonti Dutta
  • , William Knottenbelt
  • , Alex Rogers
  • , Amarjeet Singh
  • , Mani Srivastava

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

41 Scopus citations

Abstract

In this demonstration, we present an open source toolkit for evaluating non-intrusive load monitoring research; a field which aims to disaggregate a household's total electricity consumption into individual appliances. The toolkit contains: a number of importers for existing public data sets, a set of preprocessing and statistics functions, a benchmark disaggregation algorithm and a set of metrics to evaluate the performance of such algorithms. Specifically, this release of the toolkit has been designed to enable the use of large data sets by only loading individual chunks of the whole data set into memory at once for processing, before combining the results of each chunk.

Original languageEnglish
Title of host publicationBuildSys 2014 - Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings
PublisherAssociation for Computing Machinery
Pages182-183
Number of pages2
ISBN (Electronic)9781450331449
DOIs
StatePublished - Nov 3 2014
Event1st ACM International Conference on Embedded Systems for Energy-Effcient Buildings, BuildSys 2014 - Memphis, United States
Duration: Nov 3 2014Nov 6 2014

Publication series

NameBuildSys 2014 - Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings

Conference

Conference1st ACM International Conference on Embedded Systems for Energy-Effcient Buildings, BuildSys 2014
Country/TerritoryUnited States
CityMemphis
Period11/3/1411/6/14

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