Semisupervised Multilabel Deep Learning Based Nonintrusive Load Monitoring in Smart Grids

Nonintrusive load monitoring (NILM) is a technique that infers appliance-level energy consumption patterns and operation state changes based on feeder power signals. With the availability of fine-grained electric load profiles, there has been increasing interest in using this approach for demand-sid...

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Veröffentlicht in:	IEEE transactions on industrial informatics 2020-11, Vol.16 (11), p.6892-6902
Hauptverfasser:	Yang, Yandong, Zhong, Jing, Li, Wei, Gulliver, T. Aaron, Li, Shufang
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Automation & Control Systems Computer Science Computer Science, Interdisciplinary Applications Datasets Deep learning Electric appliances Energy consumption Energy management Engineering Engineering, Industrial Feature extraction Hidden Markov models Informatics load signatures Machine learning Monitoring multilabel classification nonintrusive load monitoring (NILM) Power consumption Science & Technology Semisupervised learning semisupervised learning (SSL) Signatures Smart grid Technology Transforms
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