References
- 1. G. F. Angelis, C. Timplalexis, S. Krinidis, D. Ioannidis, and D. Tzovaras, Nilm applications: Literature review of learning approaches, recent developments and challenges, Energy and Buildings, p. 111951, 2022.10.1016/j.enbuild.2022.111951
- 2. A. R. Al Ali, I. A. Zualkernan, M. Rashid, R. Gupta, and M. Alikarar, A smart home energy management system using iot and big data analytics approach, IEEE Transactions on Consumer Electronics, vol. 63, no. 4, pp. 426–434, 2017.10.1109/TCE.2017.015014
- 3. D. M. Han and J. H. Lim, Smart home energy management system using ieee 802.15.4 and zigbee, IEEE Transactions on Consumer Electronics, vol. 56, no. 3, pp. 1403–1410, 2010.10.1109/TCE.2010.5606276
- 4. F. M. Wittmann, J. C. L/opez, and M. J. Rider, Nonintrusive load monitoring algorithm using mixedinteger linear programming, IEEE Transactions on Consumer Electronics, vol. 64, no. 2, pp. 180–187, 2018.10.1109/TCE.2018.2843292
- 5. K. C. Armel, A. Gupta, G. Shrimali, and A. Albert, Is disaggregation the holy grail of energy efficiency? the case of electricity, Energy Policy, vol. 52, pp. 213–234, 2013.10.1016/j.enpol.2012.08.062
- 6. G. W. Hart, Nonintrusive appliance load monitoring, Proceedings of the IEEE, vol. 80, no. 12, pp. 1870–1891, 1992.10.1109/5.192069
- 7. E. Elhamifar and S. Sastry, Energy disaggregation via learning powerlets and sparse coding., in AAAI, pp. 629–635, AAAI, 2015.10.1609/aaai.v29i1.9249
- 8. S. Gupta, M. S. Reynolds, and S. N. Patel, Electrisense: single-point sensing using emi for electrical event detection and classification in the home, in Proceedings of the 12th ACM international conference on Ubiquitous computing, pp. 139–148, ACM, 2010.10.1145/1864349.1864375
- 9. G. Kalogridis, C. Efthymiou, S. Z. Denic, T. A. Lewis, and R. Cepeda, Privacy for smart meters: Towards undetectable appliance load signatures, in Smart Grid Communications (SmartGridComm), 2010 First IEEE International Conference on, pp. 232–237, IEEE, 2010.10.1109/SMARTGRID.2010.5622047
- 10. A. Prudenzi, A neuron nets based procedure for identifying domestic appliances pattern-of-use from energy recordings at meter panel, in Power Engineering Society Winter Meeting, 2002. IEEE, vol. 2, pp. 941–946, IEEE, 2002.
- 11. K. Basu, V. Debusschere, A. Douzal-Chouakria, and S. Bacha, Time series distance-based methods for non-intrusive load monitoring in residential buildings, Energy and Buildings, vol. 96, pp. 109–117, 2015.10.1016/j.enbuild.2015.03.021
- 12. K. Basu, V. Debusschere, S. Bacha, U. Maulik, and S. Bondyopadhyay, Nonintrusive load monitoring: A temporal multilabel classification approach, IEEE Transactions on Industrial informatics, vol. 11, no. 1, pp. 262–270, 2015.10.1109/TII.2014.2361288
- 13. H. Kim, M. Marwah, M. Arlitt, G. Lyon, and J. Han, Unsupervised disaggregation of low frequency power measurements, in Proceedings of the 2011 SIAM international conference on data mining, pp. 747–758, SIAM, 2011.10.1137/1.9781611972818.64
- 14. O. Parson, S. Ghosh, M. Weal, and A. Rogers, Non-intrusive load monitoring using prior models of general appliance types, in Twenty-Sixth AAAI Conference on Artificial Intelligence, AAAI, 2012.
- 15. A. Cominola, M. Giuliani, D. Piga, A. Castelletti, and A. E. Rizzoli, A hybrid signature-based iterative disaggregation algorithm for non-intrusive load monitoring, Applied energy, vol. 185, pp. 331–344, 2017.10.1016/j.apenergy.2016.10.040
- 16. L. Massidda and M. Marrocu, A bayesian approach to unsupervised, non-intrusive load disaggregation, Sensors, vol. 22, no. 12, p. 4481, 2022.10.3390/s22124481922926935746263
- 17. F. Hidiyanto and A. Halim, Knn methods with varied k, distance and training data to disaggregate nilm with similar load characteristic, in Proceedings of the 3rd Asia Pacific Conference on Research in Industrial and Systems Engineering 2020, pp. 93–99, ACM, 2020.10.1145/3400934.3400953
- 18. M. Singh, S. Kumar, S. Semwal, and R. Prasad, Residential load signature analysis for their segregation using wavelet - svm, in Power Electronics and Renewable Energy Systems, pp. 863–871, Springer, 2015.10.1007/978-81-322-2119-7_84
- 19. F. Gong, N. Han, Y. Zhou, S. Chen, D. Li, and S. Tian, A svm optimized by particle swarm optimization approach to load disaggregation in non-intrusive load monitoring in smart homes, in 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2), pp. 1793–1797, IEEE, 2019.10.1109/EI247390.2019.9062124
- 20. M. Hasan, D. Chowdhury, M. Khan, Z. Rahman, et al., Non-intrusive load monitoring using current shapelets, Applied Sciences, vol. 9, no. 24, p. 5363, 2019.10.3390/app9245363
- 21. Z. Xiao, W. Gang, J. Yuan, Y. Zhang, and C. Fan, Cooling load disaggregation using a nilm method based on random forest for smart buildings, Sustainable Cities and Society, vol. 74, p. 103202, 2021.10.1016/j.scs.2021.103202
- 22. X. Wu, Y. Gao, and D. Jiao, Multi-label classification based on random forest algorithm for nonintrusive load monitoring system, Processes, vol. 7, no. 6, p. 337, 2019.10.3390/pr7060337
- 23. J. Kelly and W. Knottenbelt, Neural nilm: Deep neural networks applied to energy disaggregation, in Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments, pp. 55–64, ACM, 2015.10.1145/2821650.2821672
- 24. D. Murray, L. Stankovic, V. Stankovic, S. Lulic, and S. Sladojevic, Transferability of neural network approaches for low-rate energy disaggregation, in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8330–8334, IEEE, 2019.10.1109/ICASSP.2019.8682486
- 25. M. D’Incecco, S. Squartini, and M. Zhong, Transfer learning for non-intrusive load monitoring, IEEE Transactions on Smart Grid, vol. 11, no. 2, pp. 1419–1429, 2019.10.1109/TSG.2019.2938068
- 26. J. Jiang, Q. Kong, M. D. Plumbley, N. Gilbert, M. Hoogendoorn, and D. M. Roijers, Deep learningbased energy disaggregation and on/off detection of household appliances, ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 15, no. 3, pp. 1–21, 2021.10.1145/3441300
- 27. O. Krystalakos, C. Nalmpantis, and D. Vrakas, Sliding window approach for online energy disaggregation using artificial neural networks, in Proceedings of the 10th Hellenic Conference on Artificial Intelligence, pp. 1–6, ACM, 2018.10.1145/3200947.3201011
- 28. J. Song, H. Wang, M. Du, L. Peng, S. Zhang, and G. Xu, Non-intrusive load identification method based on improved long short term memory network, Energies, vol. 14, no. 3, p. 684, 2021.10.3390/en14030684
- 29. H. C. imen, N. C. etinkaya, J. C. Vasquez, and J. M. Guerrero, A microgrid energy management system based on non-intrusive load monitoring via multitask learning, IEEE Transactions on Smart Grid, vol. 12, no. 2, pp. 977–987, 2020.10.1109/TSG.2020.3027491
- 30. M. Valenti, R. Bonfigli, E. Principi, and 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, 2018.10.1109/IJCNN.2018.8489271
- 31. A. Faustine, L. Pereira, H. Bousbiat, and S. Kulkarni, Unet-nilm: A deep neural network for multitasks appliances state detection and power estimation in nilm, in Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring, pp. 84–88, ACM, 2020.10.1145/3427771.3427859
- 32. Z. Yue, C. R. Witzig, D. Jorde, and H. A. Jacobsen, Bert4nilm: A bidirectional transformer model for non-intrusive load monitoring, in Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring, pp. 89–93, ACM, 2020.10.1145/3427771.3429390
- 33. V. Piccialli and A. M. Sudoso, Improving non-intrusive load disaggregation through an attentionbased deep neural network, Energies, vol. 14, no. 4, p. 847, 2021.10.3390/en14040847
- 34. L. Massidda, M. Marrocu, and S. Manca, Non-intrusive load disaggregation by convolutional neural network and multilabel classification, Applied Sciences, vol. 10, no. 4, p. 1454, 2020.10.3390/app10041454
- 35. L. Massidda, M. Marrocu, and S. Manca, Non-intrusive load disaggregation via a fully convolutional neural network: improving the accuracy on unseen household, in 2020 2nd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES), vol. 1, pp. 317–322, IEEE, 2020.10.1109/IESES45645.2020.9210661
- 36. R. Terracciano, V. Galdi, V. Calderaro, D. Pappalardo, G. Ceneri, and A. O. Piti, Demand side management services for smart buildings with the use of second generation smart meter and the chain-2 of e-distribuzione, in 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), pp. 1–6, IEEE, 2020.10.1109/EEEIC/ICPSEurope49358.2020.9160752
- 37. D. Serra, D. Mardero, L. Di Stefano, and S. Grillo, Post-metering value-added services for low voltage electricity users: Lessons learned from the italian experience of chain 2, Applied Energy, vol. 304, p. 117806, Dec 2021.10.1016/j.apenergy.2021.117806
- 38. H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, Pyramid scene parsing network, in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2881–2890, IEEE, 2017.10.1109/CVPR.2017.660
- 39. A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, Pytorch: An imperative style, high-performance deep learning library, in Advances in Neural Information Processing Systems 32, pp. 8024–8035, Curran Associates, Inc., 2019.
- 40. S. Vitiello, N. Andreadou, M. Ardelean, and G. Fulli, Smart metering roll-out in europe: Where do we stand? cost benefit analyses in the clean energy package and research trends in the green deal, Energies, vol. 15, no. 7, p. 2340, 2022.10.3390/en15072340
- 41. C. Staff, Cei-en 50065-1, signalling on low-voltage electrical installations in the frequency range 3 khz to 148.5 khz part 1: General requirements, frequency bands and electromagnetic disturbances, CEI Standards, 2012.
- 42. C. Staff, Cei ts 13-82:2017-08, sistemi di misura dell’energia elettrica - comunicazione con i dispositivi utente, parte 2: Modello dati e modello applicativo., CEI Standards, 2012.
- 43. J. Kelly and W. Knottenbelt, The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes, Scientific data, vol. 2, p. 150007, 2015.10.1038/sdata.2015.7
- 44. D. Murray, L. Stankovic, and V. Stankovic, An electrical load measurements dataset of united kingdom households from a two-year longitudinal study, Scientific data, vol. 4, no. 1, pp. 1–12, 2017.10.1038/sdata.2016.122531549528055033
- 45. P. Laviron, X. Dai, B. Huquet, and T. Palpanas, Electricity demand activation extraction: From known to unknown signatures, using similarity search, in Proceedings of the Twelfth ACM International Conference on Future Energy Systems, pp. 148–159, ACM, 2021.10.1145/3447555.3464865
- 46. H. Rafiq, X. Shi, H. Zhang, H. Li, and M. K. Ochani, A deep recurrent neural network for nonintrusive load monitoring based on multi-feature input space and post-processing, Energies, vol. 13, no. 9, p. 2195, 2020.10.3390/en13092195
- 47. G. Zhou, Z. Li, M. Fu, Y. Feng, X. Wang, and C. Huang, Sequence-to-sequence load disaggregation using multiscale residual neural network, IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–10, 2020.10.1109/TIM.2020.3034989
- 48. C. Puente, R. Palacios, Y. Gonz/alez-Arechavala, and E. F. S/anchez-/Ubeda, Non-intrusive load monitoring (nilm) for energy disaggregation using soft computing techniques, Energies, vol. 13, no. 12, p. 3117, 2020.10.3390/en13123117
- 49. Y. Pan, K. Liu, Z. Shen, X. Cai, and Z. Jia, Sequence-to-subsequence learning with conditional gan for power disaggregation, in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3202–3206, IEEE, 2020.10.1109/ICASSP40776.2020.9053947