Scalable Hybrid Classification-Regression Solution for High-Frequency Nonintrusive Load Monitoring: Preprint
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 Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Advanced Research Projects Agency - Energy (ARPA-E)
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1899982
- Report Number(s):
- NREL/CP-5D00-76389; MainId:6119; UUID:33341711-3b69-ea11-9c31-ac162d87dfe5; MainAdminID:67917
- Country of Publication:
- United States
- Language:
- English
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