Construction of Adaptive Feature Library and Load Identification Based on Decomposition of Non-intrusive Power Consumption Data

Xin WU; Dian JIAO; Yuchen GAO

doi:10.7500/AEPS20190612009

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Construction of Adaptive Feature Library and Load Identification Based on Decomposition of Non-intrusive Power Consumption Data

Basic Research | 更新时间：2025-06-16

- Construction of Adaptive Feature Library and Load Identification Based on Decomposition of Non-intrusive Power Consumption Data
- Automation of Electric Power Systems Vol. 44, Issue 4, Pages: 101-109(2020)
- 作者机构：
  
  华北电力大学电气与电子工程学院，北京市 102206
- 作者简介：
- 基金信息：
  
  Natural Science Foundation of Beijing Municipality
- DOI：10.7500/AEPS20190612009
  CLC：
- Received：12 June 2019，
  
  Revised：2019-09-02，
  
  Published Online：24 December 2019，
  
  Published：25 February 2020
- 稿件说明：
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武昕,焦点,高宇辰.基于非侵入式用电数据分解的自适应特征库构建与负荷辨识[J].电力系统自动化,2020,44(04):101-109.

WU Xin,JIAO Dian,GAO Yuchen.Construction of Adaptive Feature Library and Load Identification Based on Decomposition of Non-intrusive Power Consumption Data[J].Automation of Electric Power Systems,2020,44(04):101-109.
武昕,焦点,高宇辰.基于非侵入式用电数据分解的自适应特征库构建与负荷辨识[J].电力系统自动化,2020,44(04):101-109. DOI： 10.7500/AEPS20190612009.

WU Xin,JIAO Dian,GAO Yuchen.Construction of Adaptive Feature Library and Load Identification Based on Decomposition of Non-intrusive Power Consumption Data[J].Automation of Electric Power Systems,2020,44(04):101-109. DOI： 10.7500/AEPS20190612009.

摘要

非侵入负荷监测是实现需求侧测量与能效优化的有效途径。文中提出了一种高频采集模式下的非侵入式负荷在线监测方法，使整个监测过程自动化、实时化。首先，根据负荷电流的可加性原理建立了负荷分离模型，得到独立负荷波形；并结合负荷的操作特性，无需预实验获取先验数据。然后，通过贝叶斯分类模型实现负荷种类判断，从而在运行过程中为每个独立用户构建动态的负荷特征库。最后，基于库中数据，通过构建寻优模型实现负荷辨识，从而持续、实时获取负荷用电状态，并通过实际采集的用电数据验证了方法的有效性。该研究可自适应地为独立用户构建负荷特征库，改善了提前建库不具有普适性的问题，同时，基于特征库的快速寻优保证了辨识的有效性与准确性。

Abstract

Non-intrusive load monitoring (NILM) is an effective way to realize measurement on demand side and optimization of energy efficiency. This paper explores an online NILM method in high-frequency acquisition mode

which ensures the entire process automative and real-time. Firstly

a load-decomposition model is established based on the additivity of load current to obtain the independent load waveform. Moreover

combined with the operation characteristics of load

priori data are obtained without pre-experiment. Then

the load types are judged by Bayesian classification model to construct dynamic feature library of load for every independent user during operation process. Finally

load identification is realized by constructing optimization model to continuously get the state of power consumption of load in real time. The power consumption data measured in actual scenario is used to verify the effectiveness of the method. The method can adaptively construct the dynamic feature library of load for independent users

which improves the weak universality caused by establishing the database in advance. The fast optimization based on feature library ensures the effectiveness and accuracy of identification.

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Keywords

references

CORBETT J , WARDLE K , CHEN C . Toward a sustainable modern electricity grid: the effects of smart metering and program investments on demand-side management performance in the US electricity sector 2009-2012 [J]. IEEE Transactions on Engineering Management , 2018 , 65 ( 2 ): 252 - 263 .

HART G W . Nonintrusive appliance load monitoring [J]. Proceedings of the IEEE , 1992 , 80 ( 12 ): 1870 - 1891 .

祁兵，董超，武昕，等 . 基于DTW算法与稳态电流波形的非侵入式负荷辨识方法 [J]. 电力系统自动化， 2018 ， 42 ( 3 ): 70 - 76 . DOI: 10.7500/AEPS20170103001 http://dx.doi.org/10.7500/AEPS20170103001 .

QI Bing , DONG Chao , WU Xin , et al . Non-intrusive load identification method based on DTW algorithm and steady-state current waveform [J]. Automation of Electric Power Systems , 2018 , 42 ( 3 ): 70 - 76 . DOI: 10.7500/AEPS20170103001 http://dx.doi.org/10.7500/AEPS20170103001 .

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 . DOI: 10.7500/AEPS20170627011 http://dx.doi.org/10.7500/AEPS20170627011 .

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 . DOI: 10.7500/AEPS20170627011 http://dx.doi.org/10.7500/AEPS20170627011 .

HONG Y Y , CHOU J H . Nonintrusive energy monitoring for microgrids using hybrid self-organizing feature-mapping networks [J]. Energies , 2012 , 5 ( 7 ): 2578 - 2593 .

LIANG J , NG S K K , KENDALL G , et al . Load signature study: Part Ⅰ basic concept, structure, and methodology [J]. IEEE Transactions on Power Delivery , 2010 , 25 ( 2 ): 551 - 560 .

LIANG J , NG S K K , KENDALL G , et al . Load signature study: Part Ⅱ disaggregation framework, simulation, and applications [J]. IEEE Transactions on Power Delivery , 2010 , 25 ( 2 ): 561 - 569 .

WU X , HAN X , LIU L Y , et al . A load identification algorithm of frequency domain filtering under current underdetermined separation [J]. IEEE Access , 2018 , 6 : 37094-37107 .

GABALDÓN A , MOLINA R , MARÍN-PARRA A , et al . Residential end-uses disaggregation and demand response evaluation using integral transforms [J]. Journal of Modern Power Systems and Clean Energy , 2017 , 5 ( 1 ): 91 - 104 .

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 .

HE D W , DU L , YANG Y , et al . Front-end electronic circuit topology analysis for model-driven classification and monitoring of appliance loads in smart buildings [J]. IEEE Transactions on Smart Grid , 2012 , 3 ( 4 ): 2286 - 2293 .

RUZZELLI A G , NICOLAS C , SCHOOFS A , et al . Real-time recognition and profiling of appliances through a single electricity sensor [C]// 2010 7th Annual IEEE Communications Society Conference on Sensor , Mesh and Ad Hoc Communications and Networks (SECON), June 21 - 25 , 2010 , Boston, USA.

CHANG H H , CHEN K L , TSAI Y P , et al . A new measurement method for power signatures of nonintrusive demand monitoring and load identification [J]. IEEE Transactions on Industry Applications , 2012 , 48 ( 2 ): 764 - 771 .

CHANG H H , YANG H T . Applying a non-intrusive energy-management system to economic dispatch for a cogeneration system and power utility [J]. Applied Energy , 2009 , 86 ( 11 ): 2335 - 2343 .

CHANG H H . Genetic algorithms and non-intrusive energy management system based economic dispatch for cogeneration units [J]. Energy , 2011 , 36 ( 1 ): 181 - 190 .

HE K H , STANKOVIC L , LIAO J , et al . Non-intrusive load disaggregation using graph signal processing [J]. IEEE Transactions on Smart Grid , 2018 , 9 ( 3 ): 1739 - 1747 .

GILLIS J M , MORSI W G . Non-intrusive load monitoring using semi-supervised machine learning and wavelet design [J]. IEEE Transactions on Smart Grid , 2017 , 8 ( 6 ): 2648 - 2655 .

DONG M , MEIRA P C M , XU W , et al . An event window based load monitoring technique for smart meters [J]. IEEE Transactions on Smart Grid , 2012 , 3 ( 2 ): 787 - 796 .

AHMADI H , MARTI J R . Load decomposition at smart meters level using eigenloads approach [J]. IEEE Transactions on Power Systems , 2015 , 30 ( 6 ): 3425 - 3436 .

DINESH C , NETTASINGHE B W , GODALIYADDA R I , et al . Residential appliance identification based on spectral information of low frequency smart meter measurements [J]. IEEE Transactions on Smart Grid , 2016 , 7 ( 6 ): 2781 - 2792 .

LIU Y , GENG G C , GAO S , et al . Non-intrusive energy use monitoring for a group of electrical appliances [J]. IEEE Transactions on Smart Grid , 2018 , 9 ( 4 ): 3801 - 3810 .

SRINIVASAN D , NG W S , LIEW A C . Neural-network-based signature recognition for harmonic source identification [J]. IEEE Transactions on Power Delivery , 2006 , 21 ( 1 ): 398 - 405 .

AHMADI H , MARTI J R . Load decomposition at smart meters level using eigen loads approach [J]. IEEE Transactions on Power Systems , 2015 , 30 ( 6 ): 3425 - 3436 .

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