Toward Explainable NILM: Real-Time Event-Based NILM Framework for High-Frequency Data
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917017399853056 |
|---|---|
| author | Gerasimov, Grigorii Kamyshev, Ilia Hoosh, Sahar Moghimian Gryazina, Elena Ouerdane, Henni |
| author_facet | Gerasimov, Grigorii Kamyshev, Ilia Hoosh, Sahar Moghimian Gryazina, Elena Ouerdane, Henni |
| contents | Non-Intrusive Load Monitoring (NILM) is an advanced, and cost-effective technique for monitoring appliance-level energy consumption. However, its adaptability is hindered by the lack of transparency and explainability. To address this challenge, this paper presents an explainable, real-time, event-based NILM framework specifically designed for high-frequency datasets. The proposed framework ensures transparency at every stage by integrating a z-score-based event detector, appliance signature estimation, Fourier-based feature extraction, an XG-Boost classifier, and post hoc SHAP analysis. The SHAP analysis further quantifies the contribution of individual features, such as cosine of specific harmonic phases, to appliance classification. The framework is trained and evaluated on the PLAID dataset, and achieved a classification accuracy of 90% while maintaining low computational requirements and a latency of less than one second. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16841 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Toward Explainable NILM: Real-Time Event-Based NILM Framework for High-Frequency Data Gerasimov, Grigorii Kamyshev, Ilia Hoosh, Sahar Moghimian Gryazina, Elena Ouerdane, Henni Systems and Control Non-Intrusive Load Monitoring (NILM) is an advanced, and cost-effective technique for monitoring appliance-level energy consumption. However, its adaptability is hindered by the lack of transparency and explainability. To address this challenge, this paper presents an explainable, real-time, event-based NILM framework specifically designed for high-frequency datasets. The proposed framework ensures transparency at every stage by integrating a z-score-based event detector, appliance signature estimation, Fourier-based feature extraction, an XG-Boost classifier, and post hoc SHAP analysis. The SHAP analysis further quantifies the contribution of individual features, such as cosine of specific harmonic phases, to appliance classification. The framework is trained and evaluated on the PLAID dataset, and achieved a classification accuracy of 90% while maintaining low computational requirements and a latency of less than one second. |
| title | Toward Explainable NILM: Real-Time Event-Based NILM Framework for High-Frequency Data |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2501.16841 |