Abstract
The machine learning based appliance identification methods are reviewed. The application of three typical machine learning based appliance identification methods are introduced. Several experiments are carried out to evaluate the real performance of the appliance identification models based on the extreme learning machine, the support vector machine, and the random forest.
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References
Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS’95 sixth international symposium on micro machine & human science, pp 39–43
Froehlich J, Larson E, Gupta S, Cohn G, Reynolds MS, Patel SN (2010) Disaggregated end-use energy sensing for the smart grid. IEEE Pervasive Comput 10(1):28–39
Hart GW (1992) Nonintrusive appliance load monitoring. Proc IEEE 80(12):1870–1891
Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832–844
Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: IEEE international joint conference on neural networks
Jia-Yun GU, Liu JF, Chen M (2014) A modified regression prediction algorithm of large sample data based on SVM. Comput Eng
Jolliffe IT, Cadima J (2016) Principal component analysis: a review and recent developments. Philos Trans A Math Phys Eng Sci 374(2065):20150202
Kamat SP (2004) Fuzzy logic based pattern recognition technique for non-intrusive load monitoring. In: Tencon IEEE region 10 conference
Khatami A, Mirghasemi S, Khosravi A, Lim CP, Nahavandi S (2017) A new PSO-based approach to fire flame detection using K-medoids clustering. Expert Syst Appl 68(C):69–80
Kumar SU, Inbarani HH (2017) PSO-based feature selection and neighborhood rough set-based classification for BCI multiclass motor imagery task. Neural Comput Appl 28(11):3239–3258
Kwok SW, Carter C (1990) Multiple decision trees. Mach Intell Pattern Recognit 9:327–335
Rauf A, Aleisa EA (2015) PSO based automated test coverage analysis of event driven systems. Intell Autom Soft Comput 21(4):491–502
Srinivasan D, Ng WS, Liew AC (2005) Neural-network-based signature recognition for harmonic source identification. IEEE Trans Power Deliv 21(1):398–405
Yang XS (2008) Nature-inspired metaheuristic algorithms. Luniver Press
Yoan M, Antti S, Patrick B, Olli S, Christian J, Amaury L (2010) OP-ELM: optimally pruned extreme learning machine. IEEE Trans Neural Netw 21(1):158–162
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Liu, H. (2020). Machine Learning Based Appliance Identification. In: Non-intrusive Load Monitoring. Springer, Singapore. https://doi.org/10.1007/978-981-15-1860-7_6
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DOI: https://doi.org/10.1007/978-981-15-1860-7_6
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