End-to-end NILM model of industrial power data based on autoencoder transformer

Ce Li; Fanglin  Guo; Rong  Yang; Hua Wang; Bo Yao

Ce Li Energy Internet Division, State Grid Gansu Electric Power Company; School of Information Science and Engineering
Fanglin Guo Energy Internet Division, State Grid Gansu Electric Power Company
Rong Yang Energy Internet Division, State Grid Gansu Electric Power Company
Hua Wang Energy Internet Division, State Grid Gansu Electric Power Company
Bo Yao Energy Internet Division, State Grid Gansu Electric Power Company

Ariticle ID: 427

187 Views, 138 PDF Downloads

Keywords: non-intrusive load monitoring, transformer, deep learning, smart grid

Abstract

Energy detection is an important part of intelligent power consumption, and its key technology is non-intrusive load monitoring (NILM). In this study, an end-to-end model is proposed to realize the NILM of commercial power data using the autoencoder-based transformer method. Firstly, we measured the operating power of different electrical appliances across different modes and combined the operating modes of electrical appliances. Considering the relatively large number of industrial electrical appliances, to ensure accuracy, we used Autoencoder to recode and reduce the dimension of the combined coding. Secondly, the transformer model was used to train the translation of the total power consumption information sequence and the state sequence of electrical appliances. Through our model, the electrical signals to be decomposed can be translated into different electrical state codes so as to realize load energy decomposition. Finally, when our model was applied to the gas station field data, the accuracy was as high as 90.17%.

References

[1] Schelling TC. Some economics of global warming. The American Economic Review. 1992, 82(1): 1-14.

[2] Santamouris M, Cartalis C, Synnefa A, et al. On the impact of urban heat island and global warming on the power demand and electricity consumption of buildings—A review. Energy and Buildings. 2015, 98: 119-124. doi: 10.1016/j.enbuild.2014.09.052

[3] Jiang X, Ding H, Shi H, et al. Novel QoS optimization paradigm for IoT systems with fuzzy logic and visual information mining integration. Neural Computing and Applications. 2019, 32(21): 16427-16443. doi: 10.1007/s00521-019-04020-3

[4] Tuballa ML, Abundo ML. A review of the development of Smart Grid technologies. Renewable and Sustainable Energy Reviews. 2016, 59: 710-725. doi: 10.1016/j.rser.2016.01.011

[5] Zoha A, Gluhak A, Imran M, et al. Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey. Sensors. 2012, 12(12): 16838-16866. doi: 10.3390/s121216838

[6] Tsai MS, Lin YH. Modern development of an Adaptive Non-Intrusive Appliance Load Monitoring system in electricity energy conservation. Applied Energy. 2012, 96: 55-73. doi: 10.1016/j.apenergy.2011.11.027

[7] Huaiying S, Yan L, Qi Z, et al. Research on the quality Data Service Platform architecture of Intelligent electricity Meters. Journal of Physics: Conference Series. 2020, 1693(1): 012120. doi: 10.1088/1742-6596/1693/1/012120

[8] Masera M, Bompard EF, Profumo F, et al. Smart (Electricity) Grids for Smart Cities: Assessing Roles and Societal Impacts. Proceedings of the IEEE. 2018, 106(4): 613-625. doi: 10.1109/jproc.2018.2812212

[9] Wei S, Qin S, Gao W, et al. The Research of Method of Malignant Load Identification Based on Wavelet Analysis. Physics Procedia. 2012, 25: 1070-1075. doi: 10.1016/j.phpro.2012.03.201

[10] Bo L, Wenpeng L. Conceptual cloud solution architecture and application scenarios of power consumption data based on load disaggregation. Power System Technology. 2016, 40(3): 791-796.

[11] Hart GW. Nonintrusive appliance load monitoring. Proceedings of the IEEE. 1992, 80(12): 1870-1891. doi: 10.1109/5.192069

[12] Niu L, Jia H. Transient event detection algorithm for non-intrusive load monitoring. Automation of Electric Power Systems. 2011, 35(9): 30-35.

[13] Wu X, Qi B, Han L, et al. Fast non-intrusive load identification algorithm for resident load based on template filtering. Automation of Electric Power Systems. 2017, 41(02): 135-141.

[14] Wu H, Han X. Non-Intrusive Residential Load Decomposition Algorithm Solving Underdetermined Equations Based on Signal Sparsification. Power System Technology. 2017, 41(9): 3033-3040.

[15] Zhou M, Song X, Tu J. Residential electricity consumption behavior analysis based on non-intrusive load monitoring. Power System Technology. 2018, 42(10): 3268-3274.

[16] Ruyi L, Xiaohuan W, Meixuan H, et al. Application of RPROP neural network in nonintrusive load decomposition. Power System Protection and Control. 2016, 44(7): 55-61.

[17] Batra N, Kelly J, Parson O, et al. NILMTK: an open source toolkit for non-intrusive load monitoring. In: Proceedings of the Fifth International Conference on Future Energy Systems; 11-13 June 2014; Cambridge, United Kingdom. pp. 265-276. doi: 10.1145/2602044.2602051

[18] Figueiredo M, de Almeida A, Ribeiro B. Home electrical signal disaggregation for non-intrusive load monitoring (NILM) systems. Neurocomputing. 2012, 96: 66-73. doi: 10.1016/j.neucom.2011.10.037

[19] Wu Z, Wang C, Peng W, et al. Non-intrusive load monitoring using factorial hidden markov model based on adaptive density peak clustering. Energy and Buildings. 2021, 244: 111025. doi: 10.1016/j.enbuild.2021.111025

[20] Revuelta Herrero J, Lozano Murciego Á, López Barriuso A, et al. Non intrusive load monitoring (NILM): A state of the art. In: De la Prieta F, Vale Z, Antunes L, et al. (editors). Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017. Springer; 2017. Volume 619. pp. 125-138. doi: 10.1007/978-3-319-61578-3_12

[21] Zoha A, Gluhak A, Nati M, et al. Low-power appliance monitoring using Factorial Hidden Markov Models. In: Proceedings of the 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing; 2-5 April 2013; Melbourne, VIC, Australia. pp. 527-532. doi: 10.1109/issnip.2013.6529845

[22] Wittmann FM, Lopez JC, Rider MJ. Nonintrusive Load Monitoring Algorithm Using Mixed-Integer Linear Programming. IEEE Transactions on Consumer Electronics. 2018, 64(2): 180-187. doi: 10.1109/tce.2018.2843292

[23] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. Available online: https://www.semanticscholar.org/paper/Attention-is-All-you-Need-Vaswani-Shazeer/204e3073870fae3d05bcbc2f6a8e263d9b72e776 (accessed on 28 February 2024).

[24] Ridi A, Gisler C, Hennebert J. A survey on intrusive load monitoring for appliance recognition. In: Proceedings of the 2014 22nd International Conference on Pattern Recognition; 24-28 August 2014; Stockholm, Sweden. pp. 3702-3707. doi: 10.1109/ICPR.2014.636

[25] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 27-30 June 2016; Las Vegas, NV, USA. pp. 770-778.

[26] Ba JL, Kiros JR, Hinton GE. Layer normalization. Available online: https://arxiv.org/abs/1607.06450 (accessed on 28 February 2024).

[27] Kelly J, Knottenbelt W. 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; 4-5 November 2015; Seoul, South Korea. pp. 55-64. doi: 10.1145/2821650.2821672

[28] Rafiq H, Zhang H, Li H, et al. Regularized LSTM based deep learning model: first step towards real-time non-intrusive load monitoring. In: Proceedings of the 2018 IEEE International Conference on Smart Energy Grid Engineering (SEGE); 12-15 August 2018; Oshawa, ON, Canada. pp. 234-239. doi: 10.1109/SEGE.2018.8499519

[29] Zhang C, Zhong M, Wang Z, et al. Sequence-to-point learning with neural networks for non-intrusive load monitoring. Proceedings of the AAAI Conference on Artificial Intelligence. 2018, 32(1). doi: 10.1609/aaai.v32i1.11873

[30] Vinyals O, Bengio S, Kudlur M. Order matters: Sequence to sequence for sets. Available online: https://arxiv.org/abs/1511.06391 (accessed on 28 February 2024).