Non-intrusive Load Monitoring Using Water Consumption Patterns

被引:0
|
作者
Keramati, Mohammad Mehdi [1 ]
Azizi, Elnaz [1 ]
Momeni, Hamid Reza [1 ]
Beheshti, Mohammad Taghi Hamidi [1 ]
Bolouki, Sadegh [1 ]
机构
[1] Tarbiat Modares Univ, Dept Elect & Comp Engn, Tehran, Iran
关键词
Load monitoring; Non-intrusive load monitoring; Multi-label classification; Appliance signature; SMART METERS;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper, we tackle the problem of non-intrusive load monitoring (NILM). The purpose of algorithm NILM, is to disaggregate the total power consumption of a house-hold into individual consumption of appliances by analyzing changes in the power signal using analytical methods. One of the main challenges in this field is the existence of appliances consuming nearly-equal power. Different studies tried to extract and define specific features for these appliances to overcome this challenge. In this research, we incorporate the water consumption patterns of appliances into our analysis to separate otherwise-indistinguishable appliance. More precisely, we perform NILM via an event-based multi-label classification method in which water consumption patterns are employed to improve accuracy. To demonstrate the efficiency of the proposed method, numerical results are provided for four appliances of AMDP dataset.
引用
收藏
页码:979 / 984
页数:6
相关论文
共 50 条
  • [21] PATH SIGNATURES FOR NON-INTRUSIVE LOAD MONITORING
    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
  • [22] Thresholding methods in non-intrusive load monitoring
    Daniel Precioso
    David Gómez-Ullate
    The Journal of Supercomputing, 2023, 79 : 14039 - 14062
  • [23] An Overview of Non-Intrusive Load Monitoring Methodologies
    Abubakar, Isiyaku
    Khalid, S. N.
    Mustafa, M. W.
    Shareef, Hussain
    Mustapha, Mamunu
    2015 IEEE CONFERENCE ON ENERGY CONVERSION (CENCON), 2015, : 54 - 59
  • [24] Federated Learning for Non-intrusive Load Monitoring
    Meng, Zhaorui
    Xie, Xiaozhu
    Xie, Yanqi
    IAENG International Journal of Applied Mathematics, 2023, 53 (03)
  • [25] SmartM: A Non-intrusive Load Monitoring Platform
    Liu, Xiufeng
    Bolwig, Simon
    Nielsen, Per Sieverts
    BUSINESS INFORMATION SYSTEMS WORKSHOPS, BIS 2019, 2019, 373 : 424 - 434
  • [26] Online non-intrusive load monitoring: A review
    Cruz-Rangel, David
    Ocampo-Martinez, Carlos
    Diaz-Rozo, Javier
    ENERGY NEXUS, 2025, 17
  • [27] Unsupervised Disaggregation for Non-intrusive Load Monitoring
    Pattem, Sundeep
    2012 11TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2012), VOL 2, 2012, : 515 - 520
  • [28] Transfer Learning for Non-Intrusive Load Monitoring
    D'Incecco, Michele
    Squartini, Stefano
    Zhong, Mingjun
    IEEE TRANSACTIONS ON SMART GRID, 2020, 11 (02) : 1419 - 1429
  • [29] A Comprehensive Survey for Non-Intrusive Load Monitoring
    Tezde, Efe Isa
    Yildiz, Eray
    TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2022, 30 (04) : 1162 - 1186
  • [30] Targeted Adaptive Non-Intrusive Load Monitoring
    Chen, Song
    Zhao, Maojiang
    Xiong, Zuqiang
    Bai, Zhemin
    Yang, Yu
    2024 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE, I2MTC 2024, 2024,