Non-intrusive Load Disaggregation Method Based on the Mining of Equipment Operating States

ZHUANG Weijin; ZHANG Hong; FANG Guoquan; CHEN Zhong

doi:10.12204/j.issn.1000-7229.2020.08.002

PDF(3165 KB)

Electric Power Construction ›› 2020, Vol. 41 ›› Issue (8) : 9-16. DOI: 10.12204/j.issn.1000-7229.2020.08.002

Non-intrusive Load Disaggregation Method Based on the Mining of Equipment Operating States

ZHUANG Weijin¹, ZHANG Hong¹,FANG Guoquan²,CHEN Zhong²

Author information +

History +

Abstract

With the combination of users smart meter and non-intrusive load monitoring, research on load disaggregation based on low-rate power data has become the latest trend. On the basis of this, a non-intrusive load disaggregation method based on the mining of operating states is proposed in this paper. Firstly, this method detects load events and extracts power characteristic around load events. In the characteristic plane, a clustering algorithm is used to obtain clusters that represent different types of load events. Finally, among clusters, the GSP algorithm is used to mine equipment operation states that are matched with load templates stored in database to realize load disaggregation. The results of example in this paper verifies the accuracy of event detection and load disaggregation, and also verifies that the introduction of circle operation energy consumption in state mining process has an optimized effect on load disaggregation of devices with similar rated power. Accordingly, it provides a novel idea for the research of non-intrusive load disaggregation technology based on low-rate power data.

Key words

/ non-intrusive load disaggregation / event detection / event clustering / operation states mining / household load

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

ZHUANG Weijin, ZHANG Hong, FANG Guoquan, CHEN Zhong. Non-intrusive Load Disaggregation Method Based on the Mining of Equipment Operating States[J]. Electric Power Construction. 2020, 41(8): 9-16 https://doi.org/10.12204/j.issn.1000-7229.2020.08.002

References

［1］李俊雄, 黎灿兵, 曹一家, 等. 面向智能电网的互动式节能调度初探［J］. 电力系统自动化, 2013, 37（8）: 20-25.
LI Junxiong, LI Canbing, CAO Yijia, et al. Preliminary investigation on interactive energy-saving dispatch oriented to smart grid［J］. Automation of Electric Power Systems, 2013, 37（8）: 20-25.
［2］ HART G W. Nonintrusive appliance load monitoring［J］. Proceedings of the IEEE, 1992, 80（12）: 1870-1891.
［3］程祥, 李林芝, 吴浩, 等. 非侵入式负荷监测与分解研究综述［J］. 电网技术, 2016, 40（10）: 3108-3117.
CHENG Xiang, LI Linzhi, WU Hao, et al. A survey of the research on non-intrusive load monitoring and disaggregation［J］. Power System Technology, 2016, 40（10）: 3108-3117.
［4］涂京，周明，宋旭帆，等.基于监督学习的非侵入式负荷监测算法比较［J］.电力自动化设备，2018，38（12）：134-140.
TU Jing，ZHOU Ming，SONG Xufan，et al. Comparison of supervised learning-based non-intrusive load monitoring algorithms ［J］. Electric Power Automation Equipment，2018，38（12）：134-140.
［5］孙毅, 崔灿, 陆俊, 等. 基于遗传优化的非侵入式家居负荷分解方法［J］. 电网技术, 2016, 40（12）: 3912-3917.
SUN Yi, CUI Can, LU Jun, et al. A non-intrusive household load monitoring method based on genetic optimization［J］. Power System Technology, 2016, 40（12）: 3912-3917.
［6］ MAKONIN S, POPOWICH F, BAJIC′ I V, et al. Exploiting HMM sparsity to perform online real-time nonintrusive load monitoring［J］. IEEE Transactions on Smart Grid, 2016, 7（6）: 2575-2585.
［7］ KONG W C, DONG Z Y, MA J, et al. An extensible approach for non-intrusive load disaggregation with smart meter data［J］. IEEE Transactions on Smart Grid, 2018, 9（4）: 3362-3372.
［8］陈思运, 高峰, 刘烃, 等. 基于因子隐马尔可夫模型的负荷分解方法及灵敏度分析［J］. 电力系统自动化, 2016, 40（21）: 128-136.
CHEN Siyun, GAO Feng, LIU Ting, et al. Load disaggregation method based on factorial hidden Markov model and its sensitivity analysis［J］. Automation of Electric Power Systems, 2016, 40（21）: 128-136.
［9］宋旭帆, 周明, 涂京, 等. 基于k-NN结合核Fisher判别的非侵入式负荷监测方法［J］. 电力系统自动化, 2018, 42（6）: 73-80.
SONG Xufan, ZHOU Ming, TU Jing, et al. Non-intrusive load monitoring method based on k-NN and kernel Fisher discriminant［J］. Automation of Electric Power Systems, 2018, 42（6）: 73-80.
［10］ GIRI S, BERGS M. An error correction framework for sequences resulting from known state-transition models in non-intrusive load monitoring［J］. Advanced Engineering Informatics, 2017, 32: 152-162.
［11］ LIAO J, ELAFOUDI G, STANKOVIC L, et al. Non-intrusive appliance load monitoring using low-resolution smart meter data［C］//2014 IEEE International Conference on Smart Grid Communications （SmartGridComm）, 3-6 Nov. 2014, Venice, Italy. IEEE, 2014: 535-540.
［12］ ZHAO B C, STANKOVIC L, STANKOVIC V. On a training-less solution for non-intrusive appliance load monitoring using graph signal processing［J］. IEEE Access, 2016,4：1784-1799.
［13］ WILSON R C, NASSAR M R, GOLD J I. Bayesian online learning of the hazard rate in change-point problems［J］. Neural Computation, 2010, 22（9）: 2452-2476.
［14］徐青山, 娄藕蝶, 郑爱霞, 等. 基于近邻传播聚类和遗传优化的非侵入式负荷分解方法［J］. 电工技术学报, 2018, 33（16）: 3868-3878.
XU Qingshan, LOU Oudie, ZHENG Aixia, et al. A non-intrusive load decomposition method based on affinity propagation and genetic algorithm optimization［J］. Transactions of China Electrotechnical Society, 2018, 33（16）: 3868-3878.
［15］ COMANICIU D, MEER P. Mean shift: A robust approach toward feature space analysis［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24（5）: 603-619.
［16］文亚凤，崔亮节，孙毅，等.考虑状态概率因子和状态修正的非侵入式负荷分解方法［J］.电网技术，2019，43（11）：4178-4184.
WEN Yafeng， CUI Liangjie， SUN Yi， et al. Non-intrusive load decomposition method considering state probability factor and state correction ［J］. Power System Technology，2019，43 （11）：4178-4184.
［17］ ORTEGA A, FROSSARD P, KOVACEVIC J, et al. Graph signal processing: Overview, challenges, and applications［J］. Proceedings of the IEEE, 2018, 106（5）: 808-828.
［18］ STANKOVIC V, LIAO J, STANKOVIC L. A graph-based signal processing approach for low-rate energy disaggregation［C］//2014 IEEE Symposium on Computational Intelligence for Engineering Solutions （CIES）, 9-12 Dec. 2014, Orlando, FL, USA. IEEE, 2014: 81-87.
［19］燕续峰, 翟少鹏, 王治华, 等. 深度神经网络在非侵入式负荷分解中的应用［J］. 电力系统自动化, 2019, 43（1）: 126-132, 167.
YAN Xufeng, ZHAI Shaopeng, WANG Zhihua, et al. Application of deep neural network in non-intrusive load disaggregation［J］. Automation of Electric Power Systems, 2019, 43（1）: 126-132, 167.
［20］ KOLTER J Z, JOHNSON M J. REDD: A public data set for energy disaggregation research ［EB/OL］.（2011-08-01）［2019-10-10］.http://redd.csail.mit.edu./.

Funding

This work is supported by National Key Research and Development Program of China（No. 2017YFB0902600） and research program “Research and Application of Key Technology for Intelligent Dispatching and Security Early-warning of Large Power Grid” of State Grid Corporation of China （No. SGJS0000DKJS1700840）.