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