Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (6): 1796-1806.doi: 10.13229/j.cnki.jdxbgxb.20220927

Previous Articles    

Non-intrusive load monitoring via online compression and reconstruction

Qiu-zhan ZHOU(),Ze-yu JI,Cong WANG(),Jing RONG   

  1. College of Communication Engineering，Jilin University，Changchun 130012，China
  • Received:2022-07-23 Online:2024-06-01 Published:2024-07-23
  • Contact: Cong WANG E-mail:13504465154@163.com;wangcong2020@jlu.edu.cn

RICH HTML

 3   

PDF (PC)

275

Abstract:

Accurate monitoring of power loads in home scenarios often relies on complex algorithmic models that are difficult to deploy in edge devices. At the same time， massive power data poses a huge challenge to communication of grid. In response to the above issues， this paper proposes a distributed non-intrusive load monitoring method. This method calculates and identifies the operating state of the load by the LSTM load monitoring algorithm based on the attention mechanism， and distributes the load monitoring task in the cloud and the edge with the help of cloud-edge collaboration technology to solve the problem of insufficient edge computing power. Aiming at the high network bandwidth requirements brought about by cloud-side communication， the compressed sensing method based on K-SVD double sparse online dictionary is used to compress and reconstruct the load signal， which effectively alleviates the shortage of communication resources. Comparing the performance of the monitoring algorithm under different load scenarios， the results show that the load monitoring algorithm in this paper can maintain an accuracy rate of more than 95%. Experiments are conducted to verify the effectiveness of the compressed sensing method on the compression of load signals， and determine the maximum compression ratio of load data without distortion.

Key words: communication and information system, non-intrusive load monitoring, compressed sensing, distributed deployment, long short-term memory

CLC Number: 

  • TP274

Fig.1

Schematic diagram of NILM"

Fig.2

LSTM unit structure"

Fig.3

CBAM module structure"

Fig.4

Cloud-edge collaborative distributed load monitoring"

Fig.5

K-SVD online sparse dictionary learning"

Fig.6

Load signal compression and reconstruction"

Fig.7

Load monitoring algorithm performance comparison"

Fig.8

Comparison of compression reconstruction results under different sparse dictionary construction methods"

Fig.9

Signals to be learned in online learning scenarios"

Fig.10

Sparse performance comparison of different online learning methods"

Table 1

Comparison of online dictionary learning methods"

在线学习方法稀疏度执行时间/s
ODL0.9498101.34
K-SVD双稀疏在线字典学习0.95410.87

Fig.11

Signal reconstruction probabilities under different compressed signal lengths"

1 Gillis J M, Chung J A, Morsi W G. Designing new orthogonal high-order wavelets for nonintrusive load monitoring[J]. IEEE Transactions on Industrial Electronics, 2018, 65(3): 2578-2589.
2 Wang A L, Chen B X, Wang C G, et al. Non-intrusive load monitoring algorithm based on features of V⁃I trajectory[J]. Electric Power Systems Research, 2018, 157: 134-144.
3 Hart G W. Nonintrusive appliance load monitoring[J]. Proceedings of the IEEE, 1992, 80(12): 1870-1891.
4 张露. 非侵入式负荷监测算法研究[D].广州: 华南理工大学电力学院, 2019.
Zhang Lu. Research on non-intrusive load monitoring algorithms[D]. Guangzhou: School of Electrial Power Engineering, South China University of Technology, 2019
5 刘博. 非侵入式电力负荷监测与分解技术[D].天津:天津大学电气与自动化工程学院, 2014.
Liu Bo. Non-intrusive power load monitoring and disaggregation technique[D].Tianjin: School of Electrial Information Engineering, Tianjin University, 2014
6 Hassan T, Javed F, Arshad N. An empirical investigation of V-I trajectory based load signatures for non-intrusive load monitoring[J]. IEEE Transactions on Smart Grid, 2014, 5(2): 870-878.
7 Tsai M S, Lin Y H. Modern development of an adaptive non-intrusive appliance load monitoring system in electricity energy conservation[J]. Applied Energy, 2012, 96: 55-73.
8 Batra N, Kelly J, Parson O, et al. NILMTK: an open source toolkit for non-intrusive load monitoring[C]∥e-Energy 2014-Proceedings of the 5th ACM International Conference on Future Energy Systems.New York,USA : Association for Computing Machinery, 2014: 265-276.
9 王轲, 钟海旺, 余南鹏, 等. 基于seq2seq和Attention机制的居民用户非侵入式负荷分解[J] .中国电机工程学报, 2019, 39(1): 75-83, 322.
Wang Ke, Zhong Hai-wang, Yu Nan-peng, et al. Nonintrusive load monitoring based on sequence-to- sequence model with attention mechanism[J]. Proceedings of the CSEE, 2019, 39(1): 75-83, 322.
10 Yue Z, Witzig C R, Jorde D, et al. BERT4NILM: a bidirectional transformer model for non-intrusive load monitoring[C]∥NILM 2020-Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring.New York,USA :Association for Computing Machinery, 2020: 89-93.
11 凌家源. 基于事件检测与深度学习的非侵入式负荷监测算法[D]. 杭州: 浙江大学电气工程学院, 2021.
Ling Jia-yuan. Non-intrusive load monitoring based on event detection and deep learning[D].Hangzhou: College of Electrical Engineering, Zhejiang University, 2021.
12 Sun Y, Cui C, Lu J, et al. Data compression and reconstruction of smart grid customers based on compressed sensing theory[J]. International Journal of Electrical Power and Energy Systems, 2016, 83: 21-25.
13 Saavedra E, Mascaraque L, Calderon G, et al. The smart meter challenge: feasibility of autonomous indoor iot devices depending on its energy harvesting source and iot wireless technology[J]. Sensors, 2021, 21(22).
14 周学斌. 智能电网海量数据轻型化方法研究[D].武汉: 武汉大学电气与自动化学院, 2020.
Zhou Xue-bin. Research on the method of light massive data in smart grid[D].Wuhan: School of Electrical Engineering and Automation, Wuhan University, 2020.
15 王大伟, 崔国宇, 庞振江, 等. 边缘计算在多芯模组化电能表中的应用研究[J]. 单片机与嵌入式系统应用, 2019, 19(4): 30-33, 37.
Wang Da-wei, Cui Guo-yu, Pang Zhen-jiang, et al. Application research of edge computing in multi-core modular energy meter[J]. Microcontrollers & Embedded Systems, 2019, 19(4): 30-33, 37.
16 Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
17 Candes E J, Tao T. Near-optimal signal recovery from random projections: universal encoding strategies?[J]. IEEE Transactions on Information Theory, 2006, 52(12): 5406-5425.
18 Bao K, Ibrahimov K, Wagner M, et al. Enhancing neural non-intrusive load monitoring with generative adversarial networks[J]. Energy Informatics, 2018, 1(): 295-302.
19 Linh N V, Arboleya P. Deep learning application to non-intrusive load monitoring[J]. 2019 IEEE Milan PowerTech, 2019, 2019: 2-6.
20 Vinay M B, Rekha K S. A model of saliency-based visual attention for rapid scene analysis[J]. International Journal of Recent Technology and Engineering, 2019, 7(6): 412-415.
21 Liu G, Guo J. Bidirectional LSTM with attention mechanism and convolutional layer for text classification[J]. Neurocomputing, 2019, 337: 325-338.
22 Woo S, Park J, Lee J Y, et al. Cbam: Convolutional block attention module[C]∥Proceedings of the European conference on computer vision (ECCV),Copenhagen, Denmark, 2018: 3-19.
23 Rubinstein R, Zibulevsky M, Elad M. Double sparsity: learning sparse dictionaries for sparse signal approximation[J]. IEEE Transactions on Signal Processing, 2010, 58(3): 1553-1564.
24 Mazumder R, Friedman J H, Hastie T. SparseNet: coordinate descent with nonconvex penalties[J]. Journal of the American Statistical Association, 2011, 106(495): 1125-1138.
25 Engan K, Aase S O, Husoy J H. Method of optimal directions for frame design[J]∥IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings, Piscetawar NJ: IEEE, 1999, 5: 2443-2446.
26 Aharon M, Elad M, Bruckstein A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322.
27 Lu C, Shi J, Jia J. Online robust dictionary learning[C]∥Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,Portland,USA, 2013: 415-422.
28 Mairal J, Bach F, Ponce J, et al. Online learning for matrix factorization and sparse coding[J]. Journal of Machine Learning Research, 2010, 11: 19-60.
29 Labusch K, Barth E, Martinetz T. Robust and fast learning of sparse codes with stochastic gradient descent[J]. IEEE Journal on Selected Topics in Signal Processing, 2011, 5(5): 1048-1060.
30 Sulam J, Ophir B, Zibulevsky M, et al. Trainlets: dictionary learning in high dimensions[J]. IEEE Transactions on Signal Processing, 2016, 64(12): 3180-3193.
31 Huang K, Wu Y, Long C, et al. Adaptive process monitoring via online dictionary learning and its industrial application[J]. ISA Transactions, 2021, 114: 399-412.
32 Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666.
33 Blumensath T, Davies M E. Iterative thresholding for saparse approximations[J]. Journal of Fourier Analysis and Applications, 2008, 14(5/6): 629-654.
34 Baraniuk R, Davenport M, DeVore R, et al. A simple proof of the restricted isometry property for random matrices[J]. Constructive Approximation, 2008, 28(3): 253-263.
35 Baraniuk R G. Compressive sensing[J]. IEEE Signal Processing Magazine, 2007, 24(4): 118-121.
36 Candes E J, Tao T. Decoding by linear programming[J]. IEEE Transactions on Information Theory, 2005, 51(12): 4203-4215.
37 Donoho D L. For most large underdetermined systems of linear equations the minimal ℓ1-norm solution is also the sparsest solution[J]. Communications on Pure and Applied Mathematics, 2006, 59(6): 797-829.
38 Needell D, Vershynin R. Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit[J]. Foundations of Computational Mathematics, 2009, 9(3): 317-334.
39 石光明, 刘丹华, 高大化, 等. 压缩感知理论及其研究进展[J]. 电子学报, 2009, 37(5): 1070-1081.
Shi Guang-ming, Liu Dan-hua, Gao Da-hua, et al. Advances in theory and application of compressed sensing[J]. Acta Electronica Sinica, 2009, 37(5): 1070-1081.
40 Kelly J, Knottenbelt W. The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes[J]. Scientific Data, 2015, 2: 1-14.
41 Shalev-Shwartz S. Online learning and online convex optimization[J]. Foundations and Trends in Machine Learning, 2011, 4(2): 107-194.
42 Hoyer P O. Non-negative matrix factorization with sparseness constraints[J]. Journal of Machine Learning Research, 2004, 5: 1457-1469.
[1] Ling HUANG,Zuan CUI,Feng YOU,Pei-xin HONG,Hao-chuan ZHONG,Yi-xuan ZENG. Vehicle trajectory prediction model for multi-vehicle interaction scenario [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(5): 1188-1195.
[2] Qiu-zhan ZHOU,Ze-yu JI,Cong WANG,Ji-kang HU,Ming-ming LI,Yu-zhu CHEN,Xian-feng ZHOU,Ping-ping LIU. Seismic sensing perimeter security system based on cat swarm algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(4): 1187-1199.
[3] Rong-han YAO,Wen-tao XU,Wei-wei GUO. Drivers' takeover behavior and intention recognition based on factor and long short⁃term memory [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 758-771.
[4] Jian-cheng WENG,Rui-cong WEI,Han-mei HE,Hai-hui XU,Jing-jing WANG. Urban road network short-term traffic flow prediction model based on associated road chain group [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(11): 3104-3112.
[5] Qing-tian GENG,Yang ZHAO,Qing-liang LI,Fan-hua YU,Xiao-ning LI. Integrated LSTM and ARIMA method based on attention model for soil temperature [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(10): 2973-2981.
[6] Chun-yan ZENG,Kang YAN,Zhi-feng WANG,Zheng-hui WANG. Multi-scale generative adversarial network for image compressed sensing and reconstruction algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(10): 2923-2931.
[7] Jin-wu GAO,Zhi-huan JIA,Xiang-yang WANG,Hao XING. Degradation trend prediction of proton exchange membrane fuel cell based on PSO⁃LSTM [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(9): 2192-2202.
[8] Hong-liang SUN,Wei-da SHEN,Ling-ling CHEN. Bandwidth compensation algorithm for mixed services under delay quality of service constraint [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1912-1917.
[9] Da-xiang LI,Meng-si CHEN,Ying LIU. Spontaneous micro-expression recognition based on STA-LSTM [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(4): 897-909.
[10] Xue-zhi WANG,Qing-liang LI,Wen-hui LI. Spatio⁃temporal model of soil moisture prediction integrated with transfer learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 675-683.
[11] Jian CHEN,Fan YU,Lin LIN,Ming⁃hui SUN. Local ultrasound array focusing method based on multiarray synthetic aperture [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(10): 2447-2455.
[12] Zhou-zhou LIU,Qian-yun ZHANG,Xin-hua MA,Han PENG. Compressed sensing signal reconstruction based on optimized discrete differential evolution algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2246-2252.
[13] Chun-ping HOU,Chun-yue ZHAO,Zhi-peng WANG,Hai-rui TIAN. Video anomaly detection algorithm based on effective anomaly sample construction [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1823-1829.
[14] Yi-jun WANG,You-xu ZHANG,Rui-xin MIAO,Jia-min DOU. D2D resource allocation algorithm based on system outage probability in 5G [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(1): 331-339.
[15] Xin-yu JIN,Mu-han XIE, SUN-Bin. Grain information compressed sensing based on semi-tensor product approach [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(1): 379-385.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd