Int J Performability Eng ›› 2021, Vol. 17 ›› Issue (1): 74-84.doi: 10.23940/ijpe.21.01.p7.7484
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Jiali Yanga, Jiarui Chena, and Sheng Lia,b*
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Jiali Yang, Jiarui Chen, and Sheng Li. Decomposition and Identification of Non-Intrusive Load Equipment Group [J]. Int J Performability Eng, 2021, 17(1): 74-84.
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1. | G. W. Hart, “Nonintrusive Appliance Load Monitoring,” Proceedings of the IEEE, Vol. 80, No. 12, pp. 1870-1891, December 1992 |
2. | M. Wang, Y. D. Zhou, S. Zhang, Y. P. Zheng, Z. H. Liu,H. Feng, “Research on Non-Intrusive Load Monitoring Method based on Feature Difference Enhancement,” Integrated Ferroelectrics, Vol. 210, No. 1, pp. 128-141, 2020 |
3. | K. Anderson, A. Ocneanu, D. Benitez, D. Carlson,M. Berges, “BLUED: A Fully Labeled Public Dataset for Event-based Non-Intrusive Load Monitoring Research,” in Proceedings of the 2nd KDD Workshop on Data Mining Applications in Sustainability (SustKDD), pp. 1-5, USA, 2012 |
4. | J. Xiao, F. Auger, Z. X. Jing,H. Sarra, “Non-Intrusive Load Event Detection Algorithm based on Bayesian Information Criterion,” Power System Protection and Control, Vol. 46, No. 22, pp. 8-14, November 2018 |
5. | L. L.Niu and H. J. Jia, “Transient Event Detection Algorithm for Non-Intrusive Load Monitoring,” Automation of Electric Power Systems, Vol. 35, No. 9, pp. 30-35, May 2011 |
6. | L. J. Cui, Y. Sun, Y. X. Liu,Y. F. Wen, “Non-Intrusive Load Disaggregation Method Considering Time-Phased State Behavior,” Automation of Electric Power Systems, Vol. 44, No. 5, pp. 215-222+242-248, January 2020 |
7. | J. X. Chen, W. Hao, P. W. Zhang,J. Xia, “RSVP & SSVEP Hybrid EEG Stimulation and Multi-Class Event Detection,” Computer Engineering and Applications, Vol. 56, No. 15, pp. 132-139, July 2019 |
8. | Y. C. Liu, X. Wang,W. You, “Non-Intrusive Load Monitoring by Voltage-Current Trajectory Enabled Transfer Learning,” IEEE Transactions on Smart Grid, Vol. 10, No. 5, pp. 5609-5619, September 2019 |
9. | C. Min, G. Q. Wen, Z. Z. Yang, X. G. Li,B. R. Li, “Non-Intrusive Load Monitoring System based on Convolution Neural Network and Adaptive Linear Programming Boosting,” Energies, Vol. 12, No. 15, pp. 2882, July 2019 |
10. | R. Kapoor and M. M. Tripathi, “Detection and Classification of Multiple Power Signal Patterns with Volterra Series and Interval Type-2 Fuzzy Logic System,” Protection and Control of Modern Power Systems, Vol. 2, No. 1, March 2017 |
11. | L. Jiang, X. F. Ding, D. Qu,H. Y. Li, “Non-Intrusive Load Monitoring and Decomposition Method based on Decision Tree,” Journal of Mathematics in Industry, Vol. 10, No. 1, pp. 1870-1891, 2020 |
12. | S. J. Ding, X. J. Wang, J. Yong,M. Liu, “Research on Event-based Non-Intrusive Load Monitoring Method,” Building Electricity, Vol. 36, No. 7, pp. 57-64, July 2017 |
13. | J. E. Krick, J. Fraine, J. Ingalls,S. Dege, “Random Forests Applied to High-Precision Photometry Analysis with Spitzer IRAC,” The Astronomical Journal, Vol. 160, No. 3, pp. 99, August 2020 |
14. | Y. X. Lai, C. F. Lai, Y. M. Huang,H. C. Chao, “Multi-Appliance Recognition System with Hybrid SVM/GMM Classifier in Ubiquitous Smart Home,” Information Sciences, Vol. 230, No. 4, pp. 39-55, May 2013 |
15. | N. V. Vladimir, “The Nature of Statistical Learning Theory,” Springer, New York, NY, 2000 |
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