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
收藏
页码:279 / 283
页数:5
相关论文
共 50 条
[41]   Simultaneous disaggregation of multiple appliances based on non-intrusive load monitoring [J].
Hua, Dong ;
Huang, Fanqi ;
Wang, Longjun ;
Chen, Wutao .
ELECTRIC POWER SYSTEMS RESEARCH, 2021, 193
[42]   Deep Learning-Based Non-Intrusive Commercial Load Monitoring [J].
Zhou, Mengran ;
Shao, Shuai ;
Wang, Xu ;
Zhu, Ziwei ;
Hu, Feng .
SENSORS, 2022, 22 (14)
[43]   Non-intrusive load monitoring based on LSTM automatic feedback encoder [J].
Wu, Jiawei ;
Wu, Xin ;
Li, Xiang .
2024 3RD INTERNATIONAL CONFERENCE ON ENERGY AND ELECTRICAL POWER SYSTEMS, ICEEPS 2024, 2024, :1321-1324
[44]   Non-intrusive Load Identification Algorithm Based on Convolution Neural Network [J].
Zhang Y. ;
Deng C. ;
Liu Y. ;
Chen S. ;
Shi M. .
Dianwang Jishu/Power System Technology, 2020, 44 (06) :2038-2044
[45]   An Improved Steady- and Transient-State Mixed Non-Intrusive Load Monitoring Using Viterbi Algorithm [J].
Liu Y. ;
Liu C. ;
Zhao X. ;
Gao S. ;
Huang X. .
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society, 2023, 38 (19) :5241-5255
[46]   Application of Wavelet-Based Classification in Non-Intrusive Load Monitoring [J].
Gray, M. ;
Morsi, W. G. .
2015 IEEE 28TH CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE), 2015, :41-45
[47]   Review on Event Inspection Based Non-intrusive Load Monitoring Algorithms [J].
Bao H. ;
Yang S. ;
Chen Z. ;
Guo X. ;
Li J. .
Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2023, 47 (13) :94-109
[48]   Non-intrusive Load Monitoring Method Based on V-I Trajectory Color Coding [J].
Xie Y. ;
Mei F. ;
Zheng J. ;
Gao A. ;
Li X. ;
Sha H. .
Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2022, 46 (04) :93-102
[49]   Non-Intrusive Load Monitoring Method for Resident Users Based on Alternating Optimization in Graph Signal [J].
Feng R. ;
Yuan W. ;
Ge L. .
Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2022, 42 (04) :1355-1364
[50]   An unsupervised non-intrusive load monitoring method for HVAC systems of office buildings based on MSTL [J].
Su, Lihong ;
Gang, Wenjie ;
Zhang, Ying ;
Dong, Shukun ;
Tu, Zhengkai .
BUILDING SIMULATION, 2025,