Non-intrusive Load Monitoring Method Based on Improved Differential Evolution Algorithm

被引：5

作者：

Lu, Chunguang ^{[1
]}

Ma, Lvbin ^{[2
]}

Xu, Tao ^{[1
]}

Ding, Guofeng ^{[3
]}

Wu, Chenghuan ^{[3
]}

Jiang, Xuedong ^{[4
]}

机构：

[1] State Grid Zhejiang Elect Power Res Inst, Hangzhou 310014, Peoples R China

[2] Zhejiang Huayun Informat Technol Co Ltd, Hangzhou 310012, Peoples R China

[3] State Grid Ningbo Elect Power Co, Ningbo 315000, Peoples R China

[4] Zhejiang Univ, Hangzhou 310027, Peoples R China

来源：

2019 11TH INTERNATIONAL CONFERENCE ON MEASURING TECHNOLOGY AND MECHATRONICS AUTOMATION (ICMTMA 2019) | 2019年

关键词：

Household appliances; Non-intrusive load monitoring; Improved differential evolution algorithm; Signal-to-noise ratio; Load identification rate;

D O I：

10.1109/ICMTMA.2019.00069

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Non-intrusive load monitoring is an efficient way to identify the load and get the details of the user's power consumption information. This paper proposes a new non-intrusive load monitoring method based on improved differential evolution algorithm. Firstly, the advantages of non-intrusive load monitoring are briefly introduced. Secondly, the detailed procedure of load identification of household appliances by improved differential evolution algorithm is described. At last, an experimental platform is established to test the effectiveness of the proposed non-intrusive load monitoring method. By introducing the signal-to-noise ratio and load identification rate, the performance of improved differential evolution algorithm is studied.

引用

页码：279 / 283

页数：5

共 50 条

[31] PATH SIGNATURES FOR NON-INTRUSIVE LOAD MONITORING [J].

Moore, Paul ;

Iliant, Theodor-Mihai ;

Ion, Filip-Alexandru ;

Wu, Yue ;

Lyons, Terry .

2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2022, :3808-3812

[32] SmartM: A Non-intrusive Load Monitoring Platform [J].

Liu, Xiufeng ;

Bolwig, Simon ;

Nielsen, Per Sieverts .

BUSINESS INFORMATION SYSTEMS WORKSHOPS, BIS 2019, 2019, 373 :424-434

[33] Transfer Learning for Non-Intrusive Load Monitoring [J].

D'Incecco, Michele ;

Squartini, Stefano ;

Zhong, Mingjun .

IEEE TRANSACTIONS ON SMART GRID, 2020, 11 (02) :1419-1429

[34] Non-intrusive Residential Load Monitoring Method Based on CNN-BiLSTM and DTW [J].

Lin S. ;

Zhan Y. ;

Li Y. ;

Li D. .

Dianwang Jishu/Power System Technology, 2022, 46 (05) :1973-1981

[35] A Stacking Ensemble Learning Method for Non-Intrusive Appliance Load Monitoring [J].

Cheng, Xudong ;

Li, Ding ;

Hu, Wenkai ;

Xu, Jinghao .

PROCEEDINGS OF THE 36TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC 2024, 2024, :6173-6178

[36] Event Detection Based on Robust Random Cut Forest Algorithm for Non-Intrusive Load Monitoring [J].

Lu, Lingxia ;

Kang, Ju-Song ;

Yu, Miao .

JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY, 2024, 12 (06) :2019-2029

[37] A Hybrid Event Detection Approach for Non-Intrusive Load Monitoring [J].

Lu, Mengqi ;

Li, Zuyi .

IEEE TRANSACTIONS ON SMART GRID, 2020, 11 (01) :528-540

[38] Event-Driven Non-Intrusive Load Monitoring Algorithm Based on Targeted Mining Multidimensional Load Characteristics [J].

Xie, Gang ;

Wang, Hongpeng .

CHINA COMMUNICATIONS, 2023, 20 (05) :40-56

[39] A Novel Non-Intrusive Load Monitoring Algorithm for Unsupervised Disaggregation of Household Appliances [J].

Criado-Ramon, D. ;

Ruiz, L. G. B. ;

Iruela, J. R. S. ;

Pegalajar, M. C. .

INFORMATION, 2024, 15 (02)

[40] Non-intrusive Load Monitoring Based on ResNeXt Network and Transfer Learning [J].

Bao G. ;

Huang Y. .

Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2023, 47 (13) :110-120

← 1 2 3 4 5 →