IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i4p1570-d754316.html
   My bibliography  Save this article

Recovery Algorithm of Power Metering Data Based on Collaborative Fitting

Author

Listed:
  • Yukun Xu

    (Electric Power Research Institute, State Grid Shanghai Municipal Electric Power Company, Shanghai 200051, China)

  • Xiangyong Kong

    (School of Electrical Engineering & Automation, Jiangsu Normal University, Xuzhou 221116, China)

  • Zheng Zhu

    (Electric Power Research Institute, State Grid Shanghai Municipal Electric Power Company, Shanghai 200051, China)

  • Chao Jiang

    (Electric Power Research Institute, State Grid Shanghai Municipal Electric Power Company, Shanghai 200051, China)

  • Shuang Xiao

    (Electric Power Research Institute, State Grid Shanghai Municipal Electric Power Company, Shanghai 200051, China)

Abstract

Electric energy metering plays a crucial role in ensuring fair and equitable transactions between grid companies and power users. With the implementation of the State Grid Corporation’s energy Internet strategy, higher requirements have been put forward for power grid companies to reduce costs and increase efficiency and user service capabilities. Meanwhile, the accuracy and real-time requirements for electric energy measurements have also increased. Electricity information collection systems are mainly used to collect the user-side energy metering data for the power users. Attributed to communication errors, communication delays, equipment failures and other reasons, some of the collected data is missed or confused, which seriously affects the refined management and service quality of power grid companies. How to deal with such data has been one of the important issues in the fields of machine learning and data mining. This paper proposes a collaborative fitting algorithm to solve the problem of missing collected data based on latent semantics. Firstly, a tree structure of user history data is established, and then the user groups adjacent to the user with missing data are obtained from this. Finally, the missing data are recovered using the alternating least-squares matrix factorization algorithm. Through numerical verification, this method has high reliability and accuracy in recoverying the missing data.

Suggested Citation

  • Yukun Xu & Xiangyong Kong & Zheng Zhu & Chao Jiang & Shuang Xiao, 2022. "Recovery Algorithm of Power Metering Data Based on Collaborative Fitting," Energies, MDPI, vol. 15(4), pages 1-19, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1570-:d:754316
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/4/1570/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/4/1570/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Belachew, Melisew Tefera & Buono, Nicoletta Del, 2020. "Hybrid projective nonnegative matrix factorization based on α-divergence and the alternating least squares algorithm," Applied Mathematics and Computation, Elsevier, vol. 369(C).
    2. Isaías González & Antonio José Calderón & José María Portalo, 2021. "Innovative Multi-Layered Architecture for Heterogeneous Automation and Monitoring Systems: Application Case of a Photovoltaic Smart Microgrid," Sustainability, MDPI, vol. 13(4), pages 1-24, February.
    3. Xiangyu Teng & Danting Lu & Yung-ho Chiu, 2019. "Emission Reduction and Energy Performance Improvement with Different Regional Treatment Intensity in China," Energies, MDPI, vol. 12(2), pages 1-18, January.
    4. Timothy Praditia & Thilo Walser & Sergey Oladyshkin & Wolfgang Nowak, 2020. "Improving Thermochemical Energy Storage Dynamics Forecast with Physics-Inspired Neural Network Architecture," Energies, MDPI, vol. 13(15), pages 1-26, July.
    5. Chengdong Li & Zixiang Ding & Jianqiang Yi & Yisheng Lv & Guiqing Zhang, 2018. "Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction," Energies, MDPI, vol. 11(1), pages 1-26, January.
    Full references (including those not matched with items on IDEAS)

    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. Mohsen Ahmadi & Mahsa Soofiabadi & Maryam Nikpour & Hossein Naderi & Lazim Abdullah & Behdad Arandian, 2022. "Developing a Deep Neural Network with Fuzzy Wavelets and Integrating an Inline PSO to Predict Energy Consumption Patterns in Urban Buildings," Mathematics, MDPI, vol. 10(8), pages 1-17, April.
    2. Wilhelm Jan Tic & Joanna Guziałowska-Tic, 2019. "The Effect of Modifiers and Method of Application on Fine-Coal Combustion," Energies, MDPI, vol. 12(23), pages 1-15, November.
    3. Ahmed H. EL-Ebiary & Mahmoud A. Attia & Mostafa I. Marei & Mariam A. Sameh, 2022. "An Integrated Seamless Control Strategy for Distributed Generators Based on a Deep Learning Artificial Neural Network," Sustainability, MDPI, vol. 14(20), pages 1-14, October.
    4. Gul, Eid & Baldinelli, Giorgio & Bartocci, Pietro & Bianchi, Francesco & Domenghini, Piergiovanni & Cotana, Franco & Wang, Jinwen, 2022. "A techno-economic analysis of a solar PV and DC battery storage system for a community energy sharing," Energy, Elsevier, vol. 244(PB).
    5. Isaías Gomes & Rui Melicio & Victor M. F. Mendes, 2021. "Assessing the Value of Demand Response in Microgrids," Sustainability, MDPI, vol. 13(11), pages 1-16, May.
    6. Xiaosheng Peng & Kai Cheng & Jianxun Lang & Zuowei Zhang & Tao Cai & Shanxu Duan, 2021. "Short-Term Wind Power Prediction for Wind Farm Clusters Based on SFFS Feature Selection and BLSTM Deep Learning," Energies, MDPI, vol. 14(7), pages 1-18, March.
    7. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    8. César Berna-Escriche & Carlos Vargas-Salgado & David Alfonso-Solar & Alberto Escrivá-Castells, 2022. "Hydrogen Production from Surplus Electricity Generated by an Autonomous Renewable System: Scenario 2040 on Grand Canary Island, Spain," Sustainability, MDPI, vol. 14(19), pages 1-29, September.
    9. Fan, Xinyuan & Zhang, Weige & Sun, Bingxiang & Zhang, Junwei & He, Xitian, 2022. "Battery pack consistency modeling based on generative adversarial networks," Energy, Elsevier, vol. 239(PE).
    10. Ali Bagheri & Véronique Feldheim & Christos S. Ioakimidis, 2018. "On the Evolution and Application of the Thermal Network Method for Energy Assessments in Buildings," Energies, MDPI, vol. 11(4), pages 1-20, April.
    11. Babak Arbab-Zavar & Emilio J. Palacios-Garcia & Juan C. Vasquez & Josep M. Guerrero, 2021. "Message Queuing Telemetry Transport Communication Infrastructure for Grid-Connected AC Microgrids Management," Energies, MDPI, vol. 14(18), pages 1-31, September.
    12. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    13. Jason Runge & Radu Zmeureanu, 2021. "A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings," Energies, MDPI, vol. 14(3), pages 1-26, January.
    14. Maltais, Louis-Gabriel & Gosselin, Louis, 2022. "Forecasting of short-term lighting and plug load electricity consumption in single residential units: Development and assessment of data-driven models for different horizons," Applied Energy, Elsevier, vol. 307(C).
    15. Loup-Noé Lévy & Jérémie Bosom & Guillaume Guerard & Soufian Ben Amor & Marc Bui & Hai Tran, 2022. "DevOps Model Appproach for Monitoring Smart Energy Systems," Energies, MDPI, vol. 15(15), pages 1-27, July.
    16. Hwang, Jun Kwon & Yun, Geun Young & Lee, Sukho & Seo, Hyeongjoon & Santamouris, Mat, 2020. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system," Renewable Energy, Elsevier, vol. 149(C), pages 1227-1245.
    17. Gregory D. Merkel & Richard J. Povinelli & Ronald H. Brown, 2018. "Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †," Energies, MDPI, vol. 11(8), pages 1-12, August.
    18. Alessandro Burgio & Domenico Cimmino & Andrea Nappo & Luigi Smarrazzo & Giuseppe Donatiello, 2023. "An IoT-Based Solution for Monitoring and Controlling Battery Energy Storage Systems at Residential and Commercial Levels," Energies, MDPI, vol. 16(7), pages 1-21, March.
    19. Chen, Sai & Song, Yan & Ding, Yueting & Zhang, Ming & Nie, Rui, 2021. "Using long short-term memory model to study risk assessment and prediction of China’s oil import from the perspective of resilience theory," Energy, Elsevier, vol. 215(PB).
    20. Maysam Abbasi & Ehsan Abbasi & Li Li & Ricardo P. Aguilera & Dylan Lu & Fei Wang, 2023. "Review on the Microgrid Concept, Structures, Components, Communication Systems, and Control Methods," Energies, MDPI, vol. 16(1), pages 1-36, January.

    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:15:y:2022:i:4:p:1570-:d:754316. 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.