Non-intrusive Load Decomposition Based on Operation State of Electrical Appliances and Deep Learning

Ping LUO; Xingchi FAN; Jianmin ZHANG; Junjie LI

doi:10.7500/AEPS20200929001

您当前的位置：

首页 >

文章列表页 >

Non-intrusive Load Decomposition Based on Operation State of Electrical Appliances and Deep Learning

Basic Research | 更新时间：2025-06-16

- Non-intrusive Load Decomposition Based on Operation State of Electrical Appliances and Deep Learning
- Automation of Electric Power Systems Vol. 45, Issue 12, Pages: 49-56(2021)
- 作者机构：
  
  1.杭州电子科技大学自动化学院，浙江省杭州市 310018
  2.杭州电子科技大学圣光机联合学院，浙江省杭州市 310018
- 作者简介：
- 基金信息：
  
  Zhejiang Provincial Natural Science Foundation;National Natural Science Foundation of China
- DOI：10.7500/AEPS20200929001
  CLC：
- Received：29 September 2020，
  
  Revised：2021-01-14，
  
  Published Online：12 March 2021，
  
  Published：25 June 2021
- 稿件说明：
移动端阅览
罗平,樊星驰,章坚民等.基于电器运行状态和深度学习的非侵入式负荷分解[J].电力系统自动化,2021,45(12):49-56.

LUO Ping,FAN Xingchi,ZHANG Jianmin,et al.Non-intrusive Load Decomposition Based on Operation State of Electrical Appliances and Deep Learning[J].Automation of Electric Power Systems,2021,45(12):49-56.
罗平,樊星驰,章坚民等.基于电器运行状态和深度学习的非侵入式负荷分解[J].电力系统自动化,2021,45(12):49-56. DOI： 10.7500/AEPS20200929001.

LUO Ping,FAN Xingchi,ZHANG Jianmin,et al.Non-intrusive Load Decomposition Based on Operation State of Electrical Appliances and Deep Learning[J].Automation of Electric Power Systems,2021,45(12):49-56. DOI： 10.7500/AEPS20200929001.

摘要

根据不同电器运行状态数的差异，将电器分为状态复杂和状态简单2类。状态复杂电器存在多种工作状态，且前后状态有逻辑关联。因此，利用非基于事件的方法，选择能考虑过去和未来运行状态变化的双向长短期记忆网络对其进行分解，并采用树结构Parzen估计算法选择该网络的超参数以提高训练的精度。状态简单电器仅有开关状态，故利用基于事件的方法获得其投切状态，并选择多层感知器网络识别对应电器种类。最后，利用极大似然优化模型求解电器的功率序列。利用参考能量分解数据集对所提方法进行验证，结果表明该方法增强了负荷分解模型的可拓展性和抗噪声能力，在一定程度上提高了负荷分解的精度。

Abstract

According to the number of operation states of different electrical appliances

electrical appliances are divided into two types: complex-state and simple-state. Complex-state electrical appliances have multiple working states

and the prior and subsequent states are logically related. Therefore

a non-event-based method is adopted and the bidirectional long short-term memory network considering past and future operation state changes is chosen to decompose the load. In order to improve the training accuracy

the hyper-parameters of the network are chosen by using the tree-structured Parzen estimator algorithm. The simple-state electrical appliances only have on or off state

so the switching states are obtained by the event-based method. Then

the multilayer perceptron network is selected to identify the corresponding electrical appliance type. Finally

the maximum likelihood optimization model is used to solve the power sequence of the electrical appliances. The proposed method is verified by using the reference energy disaggregation data set. The results show that the method enhances the scalability and anti-noise ability of the load decomposition model

and improves the accuracy of the load decomposition to a certain extent.

关键词

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 . Non-intrusive appliance load monitoring ［J］. Proceedings of the IEEE ， 1992 ， 80 （ 12 ）： 1870 - 1891 .

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 .

邓晓平，张桂青，魏庆来，等 . 非侵入式负荷监测综述［J/OL］. 自动化学报［ 2020-09-07 ］. https：//doi.org/10.16383/j.aas.c200270 https://doi.org/10.16383/j.aas.c200270 .

DENG Xiaoping ， ZHANG Guiqing ， WEI Qinglai ， et al . A survey on the non-intrusive load monitoring ［J/OL］. Acta Automatica Sinica ［ 2020-09-07 ］. https：//doi.org/10.16383/j.aas.c200270 https://doi.org/10.16383/j.aas.c200270 .

武昕，于金莹，彭林，等 . 基于用户边缘侧事件解析的工业电力负荷非侵入式感知辨识［J］. 电力系统自动化， 2021 ， 45 （ 4 ）： 29 - 37 .

WU Xin ， YU Jinying ， PENG Lin ， et al . Non-intrusive perception and identification of industrial power load based on event analysis on user edge ［J］. Automation of Electric Power Systems ， 2021 ， 45 （ 4 ）： 29 - 37 .

HASAN T ， JAVED F ， ARSHAD N . An empirical investigation of V - I trajectory based load signatures for intrusive load monitoring ［J］. IEEE Transactions on Smart Grid ， 2014 ， 5 （ 2 ）： 870 - 878 .

周明，宋旭帆，涂京，等 . 基于非侵入式负荷监测的居民用电行为分析［J］. 电网技术， 2018 ， 42 （ 10 ）： 3268 - 3274 .

ZHOU Ming ， SONG Xufan ， TU Jing ， et al . Residential electricity consumption behavior analysis based on non-intrusive load monitoring ［J］. Power System Technology ， 2018 ， 42 （ 10 ）： 3268 - 3274 .

徐志翔 . 基于机器学习的非侵入式负荷监测技术研究［D］. 杭州：浙江大学， 2020 .

XU Zhixiang . Research on the non-intrusive load monitoring based on machine learning ［D］. Hangzhou ： Zhejiang University ， 2020 .

林顺富，于俊苏，李东东，等 . 基于二分递推SVD分解的居民电器开关事件检测方法［J］. 电网技术， 2020 ， 44 （ 4 ）： 1534 - 1542 .

LIN Shunfu ， YU Junsu ， LI Dongdong ， et al . Method of switching events detection of residential appliances based on binary recursive SVD decomposition ［J］. Power System Technology ， 2020 ， 44 （ 4 ）： 1534 - 1542 .

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 .

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 .

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 .

TONGTA A ， CHOORUANG K . Long short-term memory （LSTM） neural networks applied to energy disaggregation ［C］// International Electrical Engineering Congress ， March 4 - 6 ， 2020 ， ChiangMai， Thailand： 1 - 4 .

任文龙，许刚 . 基于深度序列翻译模型的非侵入式负荷分解方法［J］. 电网技术， 2020 ， 44 （ 1 ）： 27 - 34 .

REN Wenlong ， XU Gang . Non-intrusive load decomposition method based on deep sequence translation model ［J］. Power System Technology ， 2020 ， 44 （ 1 ）： 27 - 34 .

杨立余，陈昊，黎明，等 . 非侵入式电力负荷多目标分解框架［J］. 电力系统保护与控制， 2020 ， 48 （ 6 ）： 100 - 107 .

YANG Liyu ， CHEN Hao ， LI Ming ， et al . A framework for non-intrusive load monitoring using multi-objective evolutionary algorithms ［J］. Power System Protection and Control ， 2020 ， 48 （ 6 ）： 100 - 107 .

BERGSTRA J ， BARDENET R ， BENGIO Y ， et al . Algorithms for hyper-parameter optimization ［C］// 25th Annual Conference on Neural Information Processing Systems， December， 2011 ， Granada， Spain ： 1 - 9 .

KOLTER J Z ， JOHNSON M J . REDD： a public data a set for energy disaggreation research ［C］// SustKDD Workshop ， 2011 ， San Diego ， USA .

FREY B J ， DUECK D . Clustering by passing messages between data points ［J］. Science ， 2007 ， 315 （ 5814 ）： 972 - 976 .

CONZALEZ M E B . A framework for enabling energy-aware facilities through minimally-intrusive approaches ［D］. Pittsburgh ， USA： Carnegie Mellon University ， 2010 .

LE T T H ， KANG H ， KIM H . Household appliance classification using lower odd-numbered harmonics and the bagging decision tree ［J］. IEEE Access ， 2020 ， 8 ： 55937 - 55952 .

GRAVES A ， MOHAMED A ， HINTON G . Speech recognition with deep recurrent neural networks ［C］// IEEE International Conference on Acoustic ， October 21 ， 2013 ， Piscataway ， USA .

MONTAVON G ， ORR G B ， MULLER K R ， et al . Neural networks： tricks of the trade ［M］. New York ， USA： Springer-Verlag Berlin Heidelberg ， 2012 .

CAGLI E ， DUMAS C ， PROUFF E . Convolutional neural networks with data augmentation against jitter-based countermeasures ［C］// International Conference on Cryptographic Hardware and Embedded Systems ， September 25 - 27 ， Taipei， China： 45 - 68 .

MAVROKEFALIDIS C ， AMPELIOTIS D ， VLACHOS E ， et al . Supervised energy disaggregation using dictionary-based modelling of appliance states ［C］// PES Innovative Smart Grid Technologies Conference Europe ， October 9 - 12 ， 2017 ， Ljubljana ， Slovenia .

KONG W ， DONG Z ， 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 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Probabilistic Prediction Method for Interpretable Net Load Based on Kolmogorov-Arnold Network

Log Abnormity Detection for Distribution Master Station Based on Large Language Models

Fault Location for Active Distribution Network with High Penetration Rate Based on Improved Spatio-Temporal Graph Neural Network

Transient Stability Assessment of Power Systems Based on Multi-scale Graph Attention Network

Related Author

LIU Dong

GUO Guodong

XIN Shujun

MAO Zhihang

HUANG Jinju

SUN Yingyun

WANG Shen

WEI Xingshen

Related Institution

State Grid Economic and Technological Research Institute Co., Ltd.

State Grid Corporation of China

State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University)

School of Control and Computer Engineering, North China Electric Power University

Nanjing NARI Information and Communication Technology Co., Ltd.

⁰