An Unsupervised Electrical Appliance Modeling Framework for Non-Intrusive Load Monitoring

被引:0
作者
Liu, Bo [1 ]
Yu, Yinxin [1 ]
Luan, Wenpeng [2 ]
Zeng, Bo [3 ]
机构
[1] Tianjin Univ, Sch Elect & Automat Engn, Tianjin, Peoples R China
[2] China Elect Power Res Inst, Beijing, Peoples R China
[3] Guangxi Power Grid Co Ltd, Elect Power Res Inst, Guangxi, Peoples R China
来源
2017 IEEE POWER & ENERGY SOCIETY GENERAL MEETING | 2017年
关键词
NILM; FSM; Electrical Appliance Modeling;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Non-Intrusive Load Monitoring (NILM) is a novel and cost-effective technique for monitoring the load electricity consumption details. Building the load model of every concerned electrical appliance for modeling its electricity consumption behavior is the basis of implementing NILM. The vast majority of existing NILM approaches need to individually measure the concerned appliance in the targeted scenarios to build its appliance model before they can be put into use. This will constrain the practical application of NILM. In this paper, we propose a fully unsupervised electrical appliance finite state machine (FSM) modeling framework for NILM. Without intruding into the targeted scenarios or requiring any prior knowledge of the unmodeled appliances, it can automatically establish the FSM model for different concerned appliances only based on aggregated load event related signatures. For each appliance, the established model includes the information of the complete state set and topological structure of FSM, and the related model parameters. We believe the proposed framework can significantly improve the applicability of the existing NILM technologies, and provide a basis for the realization of auto setup NILM.
引用
收藏
页数:5
相关论文
共 50 条
  • [31] Online real-time robust framework for non-intrusive load monitoring in constrained edge devices
    Garcia-Marrero, L. E.
    Monmasson, E.
    Petrone, G.
    [J]. APPLIED ENERGY, 2025, 378
  • [32] Developing and Evaluating a Probabilistic Event Detector for Non-Intrusive Load Monitoring
    Pereira, Lucas
    [J]. 2017 FIFTH IFIP CONFERENCE ON SUSTAINABLE INTERNET AND ICT FOR SUSTAINABILITY (SUSTAINIT 2017), 2017, : 37 - 46
  • [33] Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance Evaluation
    Klemenjak, Christoph
    Makonin, Stephen
    Elmenreich, Wilfried
    [J]. 2020 IEEE POWER & ENERGY SOCIETY INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE (ISGT), 2020,
  • [34] Activity Supervision Tool using Non-Intrusive Load Monitoring Systems
    Alcala, Jose
    Urena, Jesus
    Hernandez, Alvaro
    [J]. PROCEEDINGS OF 2015 IEEE 20TH CONFERENCE ON EMERGING TECHNOLOGIES & FACTORY AUTOMATION (ETFA), 2015,
  • [35] Efficient Supervised Machine Learning Network for Non-Intrusive Load Monitoring
    Hadi, Muhammad Usman
    Suhaimi, Nik Hazmi Nik
    Basit, Abdul
    [J]. TECHNOLOGIES, 2022, 10 (04)
  • [36] An Innovative Non-Intrusive Load Monitoring System for Commercial and Industrial Application
    Kien Nguyen Trung
    Zammit, Olivier
    Dekneuvel, Eric
    Nicolle, Benjamin
    Cuong Nguyen Van
    Jacquemod, Gilles
    [J]. 2012 INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES FOR COMMUNICATIONS (ATC 2012), 2012, : 23 - 27
  • [37] Overview of Non-Intrusive Load Monitoring A way to energy wise consumption
    Antonio Hoyo-Montano, Jose
    Aberto Pereyda-Pierre, Carlos
    Manuel Tarin-Fontes, Jesus
    Naim Leon-Ortega, Jesus
    [J]. PROCEEDINGS OF THE 2016 13TH INTERNATIONAL CONFERENCE ON POWER ELECTRONICS (CIEP), 2016, : 221 - 226
  • [38] Towards Applicability: A Comparative Study on Non-Intrusive Load Monitoring Algorithms
    Ren, Huamin
    Bianchi, Filippo Maria
    Li, Jingyue
    Olsen, Rasmus L.
    Jenssen, Robert
    Anfinsen, Stian Normann
    [J]. 2021 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE), 2021,
  • [39] A Brief Review of Non-Intrusive Load Monitoring and Its Impact on Social Life
    Gurbuz, Fethi Batincan
    Bayindir, Ramazan
    Bulbul, Halil Ibrahim
    [J]. 2021 9TH INTERNATIONAL CONFERENCE ON SMART GRID, ICSMARTGRID, 2021, : 289 - 294
  • [40] Comparing four machine learning algorithms for household non-intrusive load monitoring
    Young, Thomas Lee
    Gopsill, James
    Valero, Maria
    Eikevag, Sindre
    Hicks, Ben
    [J]. ENERGY AND AI, 2024, 17