IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i15p2882-d252028.html
   My bibliography  Save this article

Non-Intrusive Load Monitoring System Based on Convolution Neural Network and Adaptive Linear Programming Boosting

Author

Listed:
  • Chao Min

    (School of Science, Southwest Petroleum University, Chengdu 610500, China
    Institute for Artificial Intelligence, Southwest Petroleum University, Chengdu 610500, China)

  • Guoquan Wen

    (School of Science, Southwest Petroleum University, Chengdu 610500, China)

  • Zhaozhong Yang

    (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China)

  • Xiaogang Li

    (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China)

  • Binrui Li

    (School of Science, Southwest Petroleum University, Chengdu 610500, China)

Abstract

Non–intrusive load monitoring based on power measurements is a promising topic of appliance identification in the research of smart grid; where the key is to avoid the power sub-item measurement in load monitoring. In this paper; a three–step non–intrusive load monitoring system (TNILM) is proposed. Firstly; a one dimension convolution neural network (CNN) is constructed based on the structure of GoogLeNet with 2D convolution; which can zoom in on the differences in features between the different appliances; and then effectively extract various transient features of appliances. Secondly; comparing with various classifiers; the Linear Programming boosting with adaptive weights and thresholds (ALPBoost) is proposed and applied to recognize single–appliance and multiple–appliance. Thirdly; an update process is adopted to adjust and balance the parameters between the one dimension CNN and ALPBoost on–line. The TNILM is tested on a real–world power consumption dataset; which comprises single or multiple appliances potentially operated simultaneously. The experiment result shows the effectiveness of the proposed method in both identification rates.

Suggested Citation

  • Chao Min & Guoquan Wen & Zhaozhong Yang & Xiaogang Li & Binrui Li, 2019. "Non-Intrusive Load Monitoring System Based on Convolution Neural Network and Adaptive Linear Programming Boosting," Energies, MDPI, vol. 12(15), pages 1-23, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:2882-:d:252028
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/15/2882/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/15/2882/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kwok Tai Chui & Miltiadis D. Lytras & Anna Visvizi, 2018. "Energy Sustainability in Smart Cities: Artificial Intelligence, Smart Monitoring, and Optimization of Energy Consumption," Energies, MDPI, vol. 11(11), pages 1-20, October.
    2. Shengli Du & Mingchao Li & Shuai Han & Jonathan Shi & Heng Li, 2019. "Multi-Pattern Data Mining and Recognition of Primary Electric Appliances from Single Non-Intrusive Load Monitoring Data," Energies, MDPI, vol. 12(6), pages 1-20, March.
    3. Cominola, A. & Giuliani, M. & Piga, D. & Castelletti, A. & Rizzoli, A.E., 2017. "A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring," Applied Energy, Elsevier, vol. 185(P1), pages 331-344.
    4. Tsai, Men-Shen & Lin, Yu-Hsiu, 2012. "Modern development of an Adaptive Non-Intrusive Appliance Load Monitoring system in electricity energy conservation," Applied Energy, Elsevier, vol. 96(C), pages 55-73.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Patrick Huber & Alberto Calatroni & Andreas Rumsch & Andrew Paice, 2021. "Review on Deep Neural Networks Applied to Low-Frequency NILM," Energies, MDPI, vol. 14(9), pages 1-34, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qian Wu & Fei Wang, 2019. "Concatenate Convolutional Neural Networks for Non-Intrusive Load Monitoring across Complex Background," Energies, MDPI, vol. 12(8), pages 1-17, April.
    2. Dinesh, Chinthaka & Welikala, Shirantha & Liyanage, Yasitha & Ekanayake, Mervyn Parakrama B. & Godaliyadda, Roshan Indika & Ekanayake, Janaka, 2017. "Non-intrusive load monitoring under residential solar power influx," Applied Energy, Elsevier, vol. 205(C), pages 1068-1080.
    3. Bonfigli, Roberto & Principi, Emanuele & Fagiani, Marco & Severini, Marco & Squartini, Stefano & Piazza, Francesco, 2017. "Non-intrusive load monitoring by using active and reactive power in additive Factorial Hidden Markov Models," Applied Energy, Elsevier, vol. 208(C), pages 1590-1607.
    4. Tekler, Zeynep Duygu & Low, Raymond & Zhou, Yuren & Yuen, Chau & Blessing, Lucienne & Spanos, Costas, 2020. "Near-real-time plug load identification using low-frequency power data in office spaces: Experiments and applications," Applied Energy, Elsevier, vol. 275(C).
    5. Tan Yigitcanlar & Kevin C. Desouza & Luke Butler & Farnoosh Roozkhosh, 2020. "Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature," Energies, MDPI, vol. 13(6), pages 1-38, March.
    6. Jia, Kunqi & Guo, Ge & Xiao, Jucheng & Zhou, Huan & Wang, Zhihua & He, Guangyu, 2019. "Data compression approach for the home energy management system," Applied Energy, Elsevier, vol. 247(C), pages 643-656.
    7. Shi, Xin & Ming, Hao & Shakkottai, Srinivas & Xie, Le & Yao, Jianguo, 2019. "Nonintrusive load monitoring in residential households with low-resolution data," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    8. Abdelhamid Zaidi & Samuel-Soma M. Ajibade & Majd Musa & Festus Victor Bekun, 2023. "New Insights into the Research Landscape on the Application of Artificial Intelligence in Sustainable Smart Cities: A Bibliometric Mapping and Network Analysis Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 287-299, July.
    9. Jie Gao & Xinping Huang & Lili Zhang, 2019. "Comparative Analysis between International Research Hotspots and National-Level Policy Keywords on Artificial Intelligence in China from 2009 to 2018," Sustainability, MDPI, vol. 11(23), pages 1-18, November.
    10. Kim, Myung Ja & Hall, C. Michael & Kwon, Ohbyung & Sohn, Kwonsang, 2024. "Space tourism: Value-attitude-behavior theory, artificial intelligence, and sustainability," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).
    11. Justyna Łapińska & Iwona Escher & Joanna Górka & Agata Sudolska & Paweł Brzustewicz, 2021. "Employees’ Trust in Artificial Intelligence in Companies: The Case of Energy and Chemical Industries in Poland," Energies, MDPI, vol. 14(7), pages 1-20, April.
    12. Zhao, Pengxiang & Dong, Zhao Yang & Meng, Ke & Kong, Weicong & Yang, Jiajia, 2021. "Household power usage pattern filtering-based residential electricity plan recommender system," Applied Energy, Elsevier, vol. 298(C).
    13. Anabel Ortega-Fernández & Rodrigo Martín-Rojas & Víctor Jesús García-Morales, 2020. "Artificial Intelligence in the Urban Environment: Smart Cities as Models for Developing Innovation and Sustainability," Sustainability, MDPI, vol. 12(19), pages 1-26, September.
    14. Jeewon Park & Oladayo S. Ajani & Rammohan Mallipeddi, 2023. "Optimization-Based Energy Disaggregation: A Constrained Multi-Objective Approach," Mathematics, MDPI, vol. 11(3), pages 1-13, January.
    15. Michał Jasiński & Tomasz Sikorski & Zbigniew Leonowicz & Klaudiusz Borkowski & Elżbieta Jasińska, 2020. "The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation," Energies, MDPI, vol. 13(9), pages 1-19, May.
    16. Liu, Bo & Luan, Wenpeng & Yu, Yixin, 2017. "Dynamic time warping based non-intrusive load transient identification," Applied Energy, Elsevier, vol. 195(C), pages 634-645.
    17. Andreea Loredana Bîrgovan & Elena Simina Lakatos & Andrea Szilagyi & Lucian Ionel Cioca & Roxana Lavinia Pacurariu & George Ciobanu & Elena Cristina Rada, 2022. "How Should We Measure? A Review of Circular Cities Indicators," IJERPH, MDPI, vol. 19(9), pages 1-16, April.
    18. Song, Chunhe & Jing, Wei & Zeng, Peng & Rosenberg, Catherine, 2017. "An analysis on the energy consumption of circulating pumps of residential swimming pools for peak load management," Applied Energy, Elsevier, vol. 195(C), pages 1-12.
    19. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Effective non-intrusive load monitoring of buildings based on a novel multi-descriptor fusion with dimensionality reduction," Applied Energy, Elsevier, vol. 279(C).
    20. Wesley Angelino de Souza & Fernando Deluno Garcia & Fernando Pinhabel Marafão & Luiz Carlos Pereira da Silva & Marcelo Godoy Simões, 2019. "Load Disaggregation Using Microscopic Power Features and Pattern Recognition," Energies, MDPI, vol. 12(14), pages 1-18, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:2882-:d:252028. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.