High-frequency, multiclass nonintrusive load monitoring for grid-interactive residential buildings

Blake Lundstrom, Govind Saraswat, Murti V. Salapaka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Smart buildings with net-load metering and control capabilities can provide valuable flexibility to grid operators. This article develops a novel approach for high-frequency, multiclass nonintrusive load monitoring (NILM) that enables effective net-load monitoring capabilities with minimal additional equipment and cost. Relative to existing NILM work, the proposed solution operates at a faster timescale, providing accurate multiclass state predictions for each 60-Hz ac cycle without relying on event-detection techniques. The approach is validated using a test bed with residential appliances and shown to have high accuracy, good generalization properties, and sufficient response time to support building grid-interactive control at fast timescales relevant to the provision of grid frequency support services.

Original languageEnglish (US)
Title of host publication2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728131030
DOIs
StatePublished - Feb 2020
Event2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020 - Washington, United States
Duration: Feb 17 2020Feb 20 2020

Publication series

Name2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020

Conference

Conference2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020
CountryUnited States
CityWashington
Period2/17/202/20/20

Bibliographical note

Funding Information:
This work was authored in part by Alliance for Sustainable Energy, LLC, the Manager and Operator of the National Renewable Energy Laboratory for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the Advanced Research Projects Agency-Energy (ARPA-E) under Grant Nos. DE-AR000701 and DE-AR0001016. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

Funding Information:
This work was authored in part by Alliance for Sustainable Energy, LLC, the Manager and Operator of the National Renewable Energy Laboratory for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the Advanced Research Projects Agency-Energy (ARPA-E) under Grant Nos. DE-AR000701 and DE-AR0001016.

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Feature extraction
  • Grid-interactive
  • Multiclass classification
  • Nonintrusive load monitoring (NILM)
  • Smart buildings

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