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The DEBS 2020 grand challenge

  • Vincenzo Gulisano
  • , Daniel Jorde
  • , Ruben Mayer
  • , Hannaneh Najdataei
  • , Dimitris Palyvos-Giannas
  • Chalmers University of Technology
  • Technical University of Munich

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

9 Scopus citations

Abstract

The ACM DEBS 2020 Grand Challenge is the tenth in a series of challenges which seek to provide a common ground and evaluation criteria for a competition aimed at both research and industrial event-based systems. The focus of the ACM DEBS 2020 Grand Challenge is on Non-Intrusive Load Monitoring (NILM). The goal of the challenge is to detect when appliances contributing to an aggregated stream of voltage and current readings from a smart meter are switched on or off. NILM is leveraged in many contexts, ranging from monitoring of energy consumption to home automation. This paper describes the specifics of the data streams provided in the challenge, as well as the benchmarking platform that supports the testing of the solutions submitted by the participants.

Original languageEnglish
Title of host publicationDEBS 2020 - Proceedings of the 14th ACM International Conference on Distributed and Event-Based Systems
PublisherAssociation for Computing Machinery
Pages183-186
Number of pages4
ISBN (Electronic)9781450380287
DOIs
StatePublished - 13 Jul 2020
Event14th ACM International Conference on Distributed and Event-Based Systems, DEBS 2020 - Montreal. Virtual, Canada
Duration: 13 Jul 202017 Jul 2020

Publication series

NameDEBS 2020 - Proceedings of the 14th ACM International Conference on Distributed and Event-Based Systems

Conference

Conference14th ACM International Conference on Distributed and Event-Based Systems, DEBS 2020
Country/TerritoryCanada
CityMontreal. Virtual
Period13/07/2017/07/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • NILM
  • data streaming
  • event processing

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