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 language | American English |
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Number of pages | 8 |
State | Published - 2022 |
Event | 2023 IEEE Conference on Innovative Smart Grid Technologies North America (ISGT NA) - Washington, D.C. Duration: 16 Jan 2023 → 19 Jan 2023 |
Conference
Conference | 2023 IEEE Conference on Innovative Smart Grid Technologies North America (ISGT NA) |
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City | Washington, D.C. |
Period | 16/01/23 → 19/01/23 |
Bibliographical note
See NREL/CP-5D00-86243 for paper as published in proceedingsNREL Publication Number
- NREL/CP-5D00-76389
Keywords
- feature extraction
- grid-interactive
- multiclass classification
- NILM
- nonintrusive load monitoring
- power prediction
- regression
- smart buildings