COLD: Concurrent Loads Disaggregator for Non-Intrusive Load Monitoring
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2021
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| _version_ | 1866911212319539200 |
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| author | Kamyshev, Ilia Hoosh, Sahar Moghimian Kriukov, Dmitrii Gryazina, Elena Ouerdane, Henni |
| author_facet | Kamyshev, Ilia Hoosh, Sahar Moghimian Kriukov, Dmitrii Gryazina, Elena Ouerdane, Henni |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2106_02352 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | COLD: Concurrent Loads Disaggregator for Non-Intrusive Load Monitoring Kamyshev, Ilia Hoosh, Sahar Moghimian Kriukov, Dmitrii Gryazina, Elena Ouerdane, Henni Signal Processing Machine Learning 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. |
| title | COLD: Concurrent Loads Disaggregator for Non-Intrusive Load Monitoring |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2106.02352 |