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arXiv · 2106.02352

COLD: Concurrent Loads Disaggregator for Non-Intrusive Load Monitoring

Ilia Kamyshev·Sahar Moghimian Hoosh·Dmitrii Kriukov·Elena Gryazina·Henni Ouerdane
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Abstract

The global effort toward renewable energy and the electrification of energy-intensive sectors have significantly increased the demand for electricity, making energy efficiency a critical focus. Non-intrusive load monitoring (NILM) enables detailed analyses of household electricity usage by disaggregating the total power consumption into individual appliance-level data. In this paper, we propose COLD (Concurrent Loads Disaggregator), a transformer-based model specifically designed to address the challenges of disaggregating high-frequency data with multiple simultaneously working devices. COLD supports up to 42 devices and accurately handles scenarios with up to 11 concurrent loads, achieving 95% load identification accuracy and 82% disaggregation performance on the test data. In addition, we introduce a new fully labeled high-frequency NILM dataset for load disaggregation derived from the UK-DALE 16 kHz dataset. Finally, we analyze the decline in NILM model performance as the number of concurrent loads increases.

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Ilia Kamyshev, Sahar Moghimian Hoosh, Dmitrii Kriukov, Elena Gryazina, Henni Ouerdane. 2021-06-04. COLD: Concurrent Loads Disaggregator for Non-Intrusive Load Monitoring. https://doi.org/10.1109/powertech59965.2025.11180295

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