Scalable Hybrid Classification-Regression Solution for High-Frequency Nonintrusive Load Monitoring

Govind Saraswat, Blake Lundstrom, Murti Salapaka

Research output: Contribution to conferencePaper

Abstract

Residential buildings with the ability to monitor and control their net-load (sum of load and generation) can provide valuable flexibility to power grid operators. We present a novel multiclass nonintrusive load monitoring (NILM) approach that enables effective net-load monitoring capabilities at high-frequency with minimal additional equipment and cost. The proposed machine learning based solution provides accurate multiclass state predictions while operating at a faster timescale (able to provide a prediction for each 60- Hz ac cycle used in US power grid) without relying on event-detection techniques. We also introduce an innovative hybrid classification-regression method that allows for the prediction of not only load on/off states but also individual load operating power levels. A test bed with eight residential appliances is used for validating the NILM approach. Results show that the overall method has high accuracy, good scaling and generalization properties.
Original languageAmerican English
Number of pages5
DOIs
StatePublished - 2023
Event2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) - Washington, D.C.
Duration: 16 Jan 202319 Jan 2023

Conference

Conference2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)
CityWashington, D.C.
Period16/01/2319/01/23

Bibliographical note

See NREL/CP-5D00-76389 for preprint

NREL Publication Number

  • NREL/CP-5D00-86243

Keywords

  • feature extraction
  • grid-interactive
  • multiclass classification
  • NILM
  • nonintrusive load monitoring
  • power prediction
  • regression
  • smart buildings
  • smart grid

Fingerprint

Dive into the research topics of 'Scalable Hybrid Classification-Regression Solution for High-Frequency Nonintrusive Load Monitoring'. Together they form a unique fingerprint.

Cite this