Abstract
The deep learning based appliance identification researches are reviewed, different schemes of deep learning application are investigated, and then several experiments are carried out to give comprehensive evaluations of deep learning based appliance identification methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Altrabalsi H, Stankovic V, Liao J, Stankovic L (2016) Low-complexity energy disaggregation using appliance load modelling. AIMS Energy 4(1):884–905
Barsim KS, Mauch L, Yang B (2018) Neural network ensembles to real-time identification of plug-level appliance measurements. arXiv:06963
Chao L, Tao J, Yang M, Li Y, Wen Z (2015) Long short term memory recurrent neural network based multimodal dimensional emotion recognition. In: 5th international workshop on audio/visual emotion challenge
De Baets L, Develder C, Dhaene T, Deschrijver D (2019) Detection of unidentified appliances in non-intrusive load monitoring using siamese neural networks. Int J Electr Power Energy Syst 104:645–653. https://doi.org/10.1016/j.ijepes.2018.07.026
Du L, He D, Harley RG, Habetler TG (2015) Electric load classification by binary voltage–current trajectory mapping. IEEE Trans Smart Grid 7(1):358–365
Hadji I, Wildes RP (2018) What do we understand about convolutional networks? arXiv:08834
Hassan T, Javed F, Arshad N (2014) An empirical investigation of V-I trajectory based load signatures for non-intrusive load monitoring. IEEE Trans Smart Grid 5(2):870–878
Huss A (2015) Hybrid model approach to appliance load disaggregation: Expressive appliance modelling by combining convolutional neural networks and hidden semi Markov models
Kelly D (2016) Disaggregation of domestic smart meter energy data
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
Lange H, Bergés M (2016) The neural energy decoder: energy disaggregation by combining binary subcomponents. In: Proceedings of the 3rd international workshop on non-intrusive load monitoring
LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324
LeCun Y, Bengio Y, Hinton GJn (2015) Deep learning. Nature 521 (7553):436
Mauch L, Barsim KS, Yang B (2016) How well can HMM model load signals. In: Proceeding of the 3rd international workshop on non-intrusive load monitoring (NILM 2016)
Tsai MS, Lin YHJAE (2012) Modern development of an adaptive non-intrusive appliance load monitoring system in electricity energy conservation. Appl Energy 96(8):55–73
Zhang C, Zhong M, Wang Z, Goddard N, Sutton C (2018) Sequence-to-point learning with neural networks for non-intrusive load monitoring. In: Thirty-second AAAI conference on artificial intelligence
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 Science Press and Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Liu, H. (2020). Deep Learning Based Appliance Identification. In: Non-intrusive Load Monitoring. Springer, Singapore. https://doi.org/10.1007/978-981-15-1860-7_8
Download citation
DOI: https://doi.org/10.1007/978-981-15-1860-7_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1859-1
Online ISBN: 978-981-15-1860-7
eBook Packages: EnergyEnergy (R0)