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Appliance classification using VI trajectories and convolutional neural networks

Leen De Baets (UGent) , Joeri Ruyssinck (UGent) , Chris Develder (UGent) , Tom Dhaene (UGent) and Dirk Deschrijver (UGent)
(2018) ENERGY AND BUILDINGS. 158. p.32-36
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Abstract
Non-intrusive load monitoring methods aim to disaggregate the total power consumption of a household into individual appliances by analysing 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. An informative characteristic to attain this goal is the voltage-current trajectory. In this paper, a weighted pixelated image of the voltage-current trajectory is used as input data for a deep learning method: a convolutional neural network that will automatically extract key features for appliance classification. The macro-average F-measure is 77.60% for the PLAID dataset and 75.46% for the WHITED dataset. (C) 2017 Elsevier B.V. All rights reserved.
Keywords
IBCN, Non-intrusive load monitoring, Appliance recognition, VI trajectory, Convolutional neural network

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Citation

Please use this url to cite or link to this publication:

MLA
De Baets, Leen et al. “Appliance Classification Using VI Trajectories and Convolutional Neural Networks.” ENERGY AND BUILDINGS 158 (2018): 32–36. Print.
APA
De Baets, L., Ruyssinck, J., Develder, C., Dhaene, T., & Deschrijver, D. (2018). Appliance classification using VI trajectories and convolutional neural networks. ENERGY AND BUILDINGS, 158, 32–36.
Chicago author-date
De Baets, Leen, Joeri Ruyssinck, Chris Develder, Tom Dhaene, and Dirk Deschrijver. 2018. “Appliance Classification Using VI Trajectories and Convolutional Neural Networks.” Energy and Buildings 158: 32–36.
Chicago author-date (all authors)
De Baets, Leen, Joeri Ruyssinck, Chris Develder, Tom Dhaene, and Dirk Deschrijver. 2018. “Appliance Classification Using VI Trajectories and Convolutional Neural Networks.” Energy and Buildings 158: 32–36.
Vancouver
1.
De Baets L, Ruyssinck J, Develder C, Dhaene T, Deschrijver D. Appliance classification using VI trajectories and convolutional neural networks. ENERGY AND BUILDINGS. Lausanne: Elsevier Science Sa; 2018;158:32–6.
IEEE
[1]
L. De Baets, J. Ruyssinck, C. Develder, T. Dhaene, and D. Deschrijver, “Appliance classification using VI trajectories and convolutional neural networks,” ENERGY AND BUILDINGS, vol. 158, pp. 32–36, 2018.
@article{8551459,
  abstract     = {Non-intrusive load monitoring methods aim to disaggregate the total power consumption of a household into individual appliances by analysing 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. An informative characteristic to attain this goal is the voltage-current trajectory. In this paper, a weighted pixelated image of the voltage-current trajectory is used as input data for a deep learning method: a convolutional neural network that will automatically extract key features for appliance classification. The macro-average F-measure is 77.60% for the PLAID dataset and 75.46% for the WHITED dataset. (C) 2017 Elsevier B.V. All rights reserved.},
  author       = {De Baets, Leen and Ruyssinck, Joeri and Develder, Chris and Dhaene, Tom and Deschrijver, Dirk},
  issn         = {0378-7788},
  journal      = {ENERGY AND BUILDINGS},
  keywords     = {IBCN,Non-intrusive load monitoring,Appliance recognition,VI trajectory,Convolutional neural network},
  language     = {eng},
  pages        = {32--36},
  publisher    = {Elsevier Science Sa},
  title        = {Appliance classification using VI trajectories and convolutional neural networks},
  url          = {http://dx.doi.org/10.1016/j.enbuild.2017.09.087},
  volume       = {158},
  year         = {2018},
}

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