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Detection of unidentified appliances in non-intrusive load monitoring using siamese neural networks

Leen De Baets (UGent) , Chris Develder (UGent) , Tom Dhaene (UGent) and Dirk Deschrijver (UGent)
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Abstract
Non-intrusive load monitoring methods aim to disaggregate the total power consumption of a household into individual appliances by analyzing changes in the voltage and current measured at the grid connection point of the household. The goal is to identify the active appliances, based on their unique fingerprint. Most state-of-the-art classification algorithms rely on the assumption that all events in the data stream are triggered by known appliances, which is often not the case. This paper proposes a method capable of detecting previously unidentified appliances in an automated way. For this, appliances represented by their VI trajectory are mapped to a newly learned feature space created by a siamese neural network such that samples of the same appliance form tight clusters. Then, clustering is performed by DBSCAN allowing the method to assign appliance samples to clusters or label them as 'unidentified'. Benchmarking on PLAID and WHITED shows that an F-1.macro-measure of respectively 0.90 and 0.85 can be obtained for classifying the unidentified appliances as 'unidentified'.
Keywords
DISAGGREGATION, MODEL, Non-intrusive load monitoring, Appliance classification, Voltage-current, trajectory, Siamese neural network

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Chicago
De Baets, Leen, Chris Develder, Tom Dhaene, and Dirk Deschrijver. 2019. “Detection of Unidentified Appliances in Non-intrusive Load Monitoring Using Siamese Neural Networks.” International Journal of Electrical Power & Energy Systems 104: 645–653.
APA
De Baets, L., Develder, C., Dhaene, T., & Deschrijver, D. (2019). Detection of unidentified appliances in non-intrusive load monitoring using siamese neural networks. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 104, 645–653.
Vancouver
1.
De Baets L, Develder C, Dhaene T, Deschrijver D. Detection of unidentified appliances in non-intrusive load monitoring using siamese neural networks. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS. Oxford: Elsevier Sci Ltd; 2019;104:645–53.
MLA
De Baets, Leen, Chris Develder, Tom Dhaene, et al. “Detection of Unidentified Appliances in Non-intrusive Load Monitoring Using Siamese Neural Networks.” INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 104 (2019): 645–653. Print.
@article{8578304,
  abstract     = {Non-intrusive load monitoring methods aim to disaggregate the total power consumption of a household into individual appliances by analyzing changes in the voltage and current measured at the grid connection point of the household. The goal is to identify the active appliances, based on their unique fingerprint. Most state-of-the-art classification algorithms rely on the assumption that all events in the data stream are triggered by known appliances, which is often not the case. This paper proposes a method capable of detecting previously unidentified appliances in an automated way. For this, appliances represented by their VI trajectory are mapped to a newly learned feature space created by a siamese neural network such that samples of the same appliance form tight clusters. Then, clustering is performed by DBSCAN allowing the method to assign appliance samples to clusters or label them as 'unidentified'. Benchmarking on PLAID and WHITED shows that an F-1.macro-measure of respectively 0.90 and 0.85 can be obtained for classifying the unidentified appliances as 'unidentified'.},
  author       = {De Baets, Leen and Develder, Chris and Dhaene, Tom and Deschrijver, Dirk},
  issn         = {0142-0615},
  journal      = {INTERNATIONAL JOURNAL OF ELECTRICAL POWER \& ENERGY SYSTEMS},
  keyword      = {DISAGGREGATION,MODEL,Non-intrusive load monitoring,Appliance classification,Voltage-current,trajectory,Siamese neural network},
  language     = {eng},
  pages        = {645--653},
  publisher    = {Elsevier Sci Ltd},
  title        = {Detection of unidentified appliances in non-intrusive load monitoring using siamese neural networks},
  url          = {http://dx.doi.org/10.1016/j.ijepes.2018.07.026},
  volume       = {104},
  year         = {2019},
}

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