Open Access
Issue
E3S Web Conf.
Volume 351, 2022
10th International Conference on Innovation, Modern Applied Science & Environmental Studies (ICIES’2022)
Article Number 01021
Number of page(s) 6
DOI https://doi.org/10.1051/e3sconf/202235101021
Published online 24 May 2022
  1. R. Gopinath, M. Kumar, C. P. C. Joshua, K. Srinivas, Energy management using non-intrusive load monitoring techniques-State-of-the-art and future research directions. Sustainable Cities and Society 62, 102411 (2020). [Google Scholar]
  2. Y. Liu, W. Liu, Y. Shen, X. Zhao, S. Gao, Toward smart energy user: Real time non-intrusive load monitoring with simultaneous switching operations. Applied Energy 287, 116616 (2021). [CrossRef] [Google Scholar]
  3. S. Hosseini, K. Agbossou, S. Kelouwani, A. Cardenas, Non-intrusive load monitoring through home energy management systems: A comprehensive review. Renewable and Sustainable Energy Reviews 79, 1266–1274 (2017). [Google Scholar]
  4. A. Zoha, A. Gluhak, M. Imran, S. Rajasegarar, Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey. Sensors 12 (12), 16838–16866 (2012). [Google Scholar]
  5. X. Yuan, P. Han, Y. Duan, R. E. Alden, V. Rallabandi, D. M. Ionel, Residential Electrical Load Monitoring and Modeling-State of the Art and Future Trends for Smart Homes and Grids. Electric Power Components and Systems 48 (11), 1125–1143 (2020). [CrossRef] [Google Scholar]
  6. S. Makonin, F. Popowich, Nonintrusive load monitoring (NILM) performance evaluation. Energy Efficiency 8 (4), 809–814 (2015). [CrossRef] [Google Scholar]
  7. G. W. Hart, Nonintrusive appliance load monitoring. Proceedings of the IEEE 80 (12), 1870–1891 (1992). [Google Scholar]
  8. O. Parson, S. Ghosh, M. Weal, A. Rogers, Nonintrusive load monitoring using prior models of general appliance types. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 26, pp. 356–362. (2012). [Google Scholar]
  9. J. Z. Kolter, T. Jaakkola, Approximate inference in additive factorial hmms with application to energy disaggregation. In: Artificial intelligence and statistics, vol. 22, pp. 1472–1482. PMLR (2012) [Google Scholar]
  10. S. Rahimi, A. D. Chan, R. A. Goubran, Nonintrusive load monitoring of electrical devices in health smart homes. In: 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings, pp. 2313–2316. IEEE (2012). [CrossRef] [Google Scholar]
  11. M. Figueiredo, A. De Almeida, B. Ribeiro, Home electrical signal disaggregation for non-intrusive load monitoring (NILM) systems. Neurocomputing 96, 66–73 (2012). [CrossRef] [Google Scholar]
  12. T.T. Thiruvaran, Phung, E. Ambikairajah, Automatic identification of electric loads using switching transient current signals. In: IEEE 2013 Tencon-Spring, pp. 252–256. IEEE (2013). [CrossRef] [Google Scholar]
  13. A. Zoha A. Gluhak, M. A. Imran, Low-power appliance monitoring using factorial hidden markov models. In: 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, pp. 527–532. IEEE (2013). [Google Scholar]
  14. V. Stankovic, J. Liao, L. Stankovic, A graph-based signal processing approach for low-rate energy disaggregation. In: 2014 IEEE symposium on computational intelligence for engineering solutions (CIES), pp. 81–87. IEEE (2014). [CrossRef] [Google Scholar]
  15. J. Kelly, W. Knottenbelt, Neural nilm: Deep neural networks applied to energy disaggregation. In: Proceedings of the 2nd ACM international conference on embedded systems for energyefficient built environments, pp. 55–64. (2015). [Google Scholar]
  16. R. Bonfigli, E. Principi, M. Severini, M. Squartini, F. Piazza, Non-intrusive load monitoring by using active and reactive power in additive Factorial Hidden Markov Models. Applied Energy 208, 1590–1607 (2017). [CrossRef] [Google Scholar]
  17. R. Bonfigli, A. Felicetti, E. Principi, M. Fagiani, S. Squartini, F. Piazza, Denoising autoencoders for non-intrusive load monitoring: improvements and comparative evaluation. Energy and Buildings 158, 1461–1474 (2018). [CrossRef] [Google Scholar]
  18. Z. Lan, B. Yin, T. Wang, G. Zuo, A non-intrusive load identification method based on convolution neural network. In: 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), pp. 1–5, IEEE (2017). [Google Scholar]
  19. D. Paiva Penha, A. R. G. Castro, Convolutional neural network applied to the identification of residential equipment in non-intrusive load monitoring systems. In: 3rd International Conference on Artificial Intelligence and Applications, pp. 11–21. (2017). [CrossRef] [Google Scholar]
  20. M. Valenti, R. Bonfigli, E. Principi, S. Squartini, Exploiting the reactive power in deep neural models for non-intrusive load monitoring. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2018). [Google Scholar]
  21. L. De Baets, J. Ruyssinck, C. Develder, T. Dhaene, D. Deschrijver, Appliance classification using VI trajectories and convolutional neural networks. Energy and Buildings 158, 32–36 (2018). [CrossRef] [Google Scholar]
  22. K. Chen, Q. Wang, Z. He, K. Chen, J. Hu, J. He, Convolutional sequence to sequence non-intrusive load monitoring. The Journal of Engineering 17, 1860–1864 (2018). [Google Scholar]
  23. A. Kundu, G. P. Juvekar, K. Davis, Deep Neural Network Based Non-Intrusive Load Status Recognition. In: 2018 Clemson University Power Systems Conference (PSC), pp. 1–6. IEEE (2018). [Google Scholar]
  24. A. L. Wang, B. X. Chen, C. G. Wang, D. Hua, Non-intrusive load monitoring algorithm based on features of V-I trajectory. Electric Power Systems Research 157, 134–144 (2018). [CrossRef] [Google Scholar]
  25. X. Shi, H. Ming, S. Shakkottai, L. Xie, J. Yao, Nonintrusive load monitoring in residential households with low-resolution data. Applied Energy 252, 113283 (2019). [CrossRef] [Google Scholar]
  26. A. Alkhulaifi, A. J. Aljohani, Investigation of deep learning-based techniques for load disaggregation, low-frequency approach. Int. J. Adv. Comput. Sci. Appl 11 (1), 701–706 (2020). [Google Scholar]
  27. M. Xia, K. Wang, W. Song, C. Chen, Y. Li, Nonintrusive load disaggregation based on composite deep long short-term memory network. Expert Systems with Applications 160, 113669 (2020). [CrossRef] [Google Scholar]
  28. X. Huang, B. Yin, Z. Wei, X. Wei, R. Zhang, An online non-intrusive load monitoring method based on Hidden Markov model. Journal of Physics: Conference Series 1176(4), 042036 (2019). [CrossRef] [Google Scholar]
  29. D. Xia, S. Ba, A. Ahmadpour, Non-intrusive load disaggregation of smart home appliances using the IPPO algorithm and FHM model. Sustainable Cities and Society 67, 102731 (2021). [Google Scholar]
  30. S. Makonin, F. Popowich, I. V. Bajić, B. Gill, L. Bartram, Exploiting HMM sparsity to perform online real-time nonintrusive load monitoring. IEEE Transactions on smart grid 7 (6), 2575–2585 (2015). [Google Scholar]
  31. Z. Wu, C. Wang, W. Peng, W. Liu, H. Zhang, Non intrusive load monitoring using factorial hidden markov model based on adaptive density peak clustering. Energy and Buildings 244, 111025 (2021). [CrossRef] [Google Scholar]
  32. H. Salem, M. Sayed-Mouchaweh, M. Tagina, Unsupervised Bayesian Non Parametric approach for Non-Intrusive Load Monitoring based on time of usage. Neurocomputing 435, 239–252 (2021). [CrossRef] [Google Scholar]
  33. G. A. Raiker, S. B. Reddy, L. Umanand, A. Yadav, M. M. Shaikh, Approach to non-intrusive load monitoring using factorial hidden markov model. In: 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS), pp. 381–386. IEEE (2018). [Google Scholar]
  34. J. Holweger, M. Dorokhova, L. Bloch, C. Ballif, N. Wyrsch, Unsupervised algorithm for disaggregating low-sampling-rate electricity consumption of households. Sustainable Energy, Grids and Networks 19, 100244 (2019). [Google Scholar]
  35. C. Yang, Z. Wu, Research on Non-intrusive Load Decomposition Based on FHMM. In: IOP Conference Series: Materials Science and Engineering, vol. 768, pp. 062046. IOP Publishing (2020). [CrossRef] [Google Scholar]
  36. R. V. A. Monteiro, J. C. R. de Santana, R. F. S. Teixeira, A. S. Bretas, R. Aguiar, C. E. P. Poma, Non-intrusive load monitoring using artificial intelligence classifiers: Performance analysis of machine learning techniques. Electric Power Systems Research 198, 107347 (2021). [CrossRef] [Google Scholar]
  37. Z. Jia, L. Yang, Z. Zhang, H. Liu, F. Kong, Sequence to point learning based on bidirectional dilated residual network for non-intrusive load monitoring. International Journal of Electrical Power & Energy Systems 129, 106837 (2021). [CrossRef] [Google Scholar]
  38. Y. Himeur, A. Alsalemi, F. Bensaali, A. Amira, Smart non-intrusive appliance identification using a novel local power histogramming descriptor with an improved k-nearest neighbors classifier. Sustainable Cities and Society 67, 102764 (2021). [Google Scholar]

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