Non-Intrusive Load Monitoring: an Architecture and its evaluation for Power Electronics loads

被引:0
|
作者
Renaux, Douglas P. B. [1 ]
Erig Lima, Carlos R. [1 ]
Pottker, Fabiana [1 ]
Oroski, Elder [1 ]
Lazzaretti, Andre E. [1 ]
Linhares, Robson R. [1 ]
Almeida, Andressa R. [1 ]
Coelho, Adil O. [1 ]
Hercules, Mateus C. [1 ]
机构
[1] Fed Univ Technol Parana UTFPR, Grad Sch Appl Comp PPGCA, Grad Sch Elect Engn & Ind Comp Sci CPGEI, Lab Embedded Syst Innovat & Technol LIT CITEC, BR-80230901 Curitiba, PR, Brazil
关键词
NILM; Non-Intrusive Load Monitoring; NILM Dataset; NILM Architecture; COOLL dataset;
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
NILM (Non-Intrusive Load Monitoring) may well become a widespread solution for diagnostic of Electrical Energy consumption available to every end user. Such a diagnostic may identify waste and improper use; it is also an important tool for energy management both by the residential users and by commercial/industrial users. An architecture for a NILM solution is proposed and evaluated. A comparison is performed among common NILM event detection algorithms and the algorithms proposed in this work. Of particular interest in this study is the detection and classification of power electronics loads, as they impose specific challenges in their detection and correct disaggregation (classification). Our proposed algorithm achieved 100% detection of on/off events for the loads in the COOLL dataset.
引用
收藏
页码:890 / 895
页数:6
相关论文
共 50 条
  • [31] An Introduction of Non-intrusive Load Monitoring and Its Challenges in System Framework
    Liu, Qi
    Lu, Min
    Liu, Xiaodong
    Linge, Nigel
    CLOUD COMPUTING AND SECURITY, ICCCS 2016, PT II, 2016, 10040 : 254 - 263
  • [32] Denoising autoencoders for Non-Intrusive Load Monitoring: Improvements and comparative evaluation
    Bonfigli, Roberto
    Felicetti, Andrea
    Principi, Emanuele
    Fagiani, Marco
    Squartini, Stefano
    Piazza, Francesco
    ENERGY AND BUILDINGS, 2018, 158 : 1461 - 1474
  • [33] Exploiting the Reactive Power in Deep Neural Models for Non-Intrusive Load Monitoring
    Valenti, Michele
    Bonfigli, Roberto
    Principi, Emanuele
    Squartini, Stefano
    2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2018,
  • [34] Towards the Fusion of Intrusive and Non-intrusive Load Monitoring - A Hybrid Approach
    Voelker, Benjamin
    Scholl, Philipp M.
    Schubert, Tobias
    Becker, Bernd
    E-ENERGY'18: PROCEEDINGS OF THE 9TH ACM INTERNATIONAL CONFERENCE ON FUTURE ENERGY SYSTEMS, 2018, : 436 - 438
  • [35] Hollow village detection method based on non-intrusive power load monitoring
    Liu, Rui
    Wang, Donglai
    Chen, Yan
    Guo, Rui
    Shi, Jiaqi
    ENERGY REPORTS, 2023, 9 : 407 - 415
  • [36] Hollow village detection method based on non-intrusive power load monitoring
    Liu, Rui
    Wang, Donglai
    Chen, Yan
    Guo, Rui
    Shi, Jiaqi
    ENERGY REPORTS, 2023, 9 : 407 - 415
  • [37] Photovoltaic Power Disaggregation using a Non-Intrusive Load Monitoring Regression Model
    Jaramillo, Andres F. Moreno
    Mohamed, Ahmed A. Raouf
    Laverty, David
    del Rincon, Jesus Martinez
    Foley, Aoife M.
    2021 IEEE PES INNOVATIVE SMART GRID TECHNOLOGY EUROPE (ISGT EUROPE 2021), 2021, : 1043 - 1048
  • [38] Identifying Variable-Power Appliances in Non-intrusive Load Monitoring Systems
    Chen, Hung-Yuan
    Fan, Yao-Chung
    Lai, Chien-Liang
    Chen, Huan
    2016 10TH INTERNATIONAL CONFERENCE ON INNOVATIVE MOBILE AND INTERNET SERVICES IN UBIQUITOUS COMPUTING (IMIS), 2016, : 452 - 457
  • [39] Phase noise as power characteristic of individual appliance for non-intrusive load monitoring
    Lee, D.
    ELECTRONICS LETTERS, 2018, 54 (16) : 994 - 995
  • [40] Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring
    Cannas, Barbara
    Carcangiu, Sara
    Carta, Daniele
    Fanni, Alessandra
    Muscas, Carlo
    APPLIED SCIENCES-BASEL, 2021, 11 (02): : 1 - 14