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

Conference ·

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.

Research Organization:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Advanced Research Projects Agency - Energy (ARPA-E)
DOE Contract Number:
AC36-08GO28308
OSTI ID:
1973681
Report Number(s):
NREL/CP-5D00-86243; MainId:87016; UUID:a428e56f-7e96-4874-8378-48d886860159; MainAdminID:69476
Resource Relation:
Conference: Presented at the 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 16-19 January 2023, Washington, D.C.; Related Information: 76389
Country of Publication:
United States
Language:
English

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conference November 2015