IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i19p6921-d1252366.html
   My bibliography  Save this article

An IHPO-WNN-Based Federated Learning System for Area-Wide Power Load Forecasting Considering Data Security Protection

Author

Listed:
  • Bujin Shi

    (Kunming Power Supply Bureau, Yunnan Power Grid Co., Ltd., Kunming 650011, China)

  • Xinbo Zhou

    (Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China)

  • Peilin Li

    (Kunming Power Supply Bureau, Yunnan Power Grid Co., Ltd., Kunming 650011, China)

  • Wenyu Ma

    (Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, China)

  • Nan Pan

    (Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, China)

Abstract

With the rapid growth of power demand and the advancement of new power system intelligence, smart energy measurement system data quality and security are also facing the influence of diversified factors. To solve the series of problems such as low data prediction efficiency, poor security perception, and “data islands” of the new power system, this paper proposes a federated learning system based on the Improved Hunter–Prey Optimizer Optimized Wavelet Neural Network (IHPO-WNN) for the whole-domain power load prediction. An improved HPO algorithm based on Sine chaotic mapping, dynamic boundaries, and a parallel search mechanism is first proposed to improve the prediction and generalization ability of wavelet neural network models. Further considering the data privacy in each station area and the potential threat of cyber-attacks, a localized differential privacy-based federated learning architecture for load prediction is designed by using the above IHPO-WNN as a base model. In this paper, the actual dataset of a smart energy measurement master station is selected, and simulation experiments are carried out through MATLAB software to test and examine the performance of IHPO-WNN and the federal learning system, respectively, and the results show that the method proposed in this paper has high prediction accuracy and excellent practical performance.

Suggested Citation

  • Bujin Shi & Xinbo Zhou & Peilin Li & Wenyu Ma & Nan Pan, 2023. "An IHPO-WNN-Based Federated Learning System for Area-Wide Power Load Forecasting Considering Data Security Protection," Energies, MDPI, vol. 16(19), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6921-:d:1252366
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/19/6921/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/19/6921/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bashiri Behmiri, Niaz & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks," Energy, Elsevier, vol. 278(C).
    2. Jiang, Yuqi & Gao, Tianlu & Dai, Yuxin & Si, Ruiqi & Hao, Jun & Zhang, Jun & Gao, David Wenzhong, 2022. "Very short-term residential load forecasting based on deep-autoformer," Applied Energy, Elsevier, vol. 328(C).
    3. Bu, Xiangya & Wu, Qiuwei & Zhou, Bin & Li, Canbing, 2023. "Hybrid short-term load forecasting using CGAN with CNN and semi-supervised regression," Applied Energy, Elsevier, vol. 338(C).
    4. Xian, Huafeng & Che, Jinxing, 2022. "Multi-space collaboration framework based optimal model selection for power load forecasting," Applied Energy, Elsevier, vol. 314(C).
    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. Islam, Md. Zahidul & Lin, Yuzhang & Vokkarane, Vinod M. & Yu, Nanpeng, 2023. "Robust learning-based real-time load estimation using sparsely deployed smart meters with high reporting rates," Applied Energy, Elsevier, vol. 352(C).
    2. Wu, Jiahui & Wang, Jidong & Kong, Xiangyu, 2022. "Strategic bidding in a competitive electricity market: An intelligent method using Multi-Agent Transfer Learning based on reinforcement learning," Energy, Elsevier, vol. 256(C).
    3. Xian, Sidong & Feng, Miaomiao & Cheng, Yue, 2023. "Incremental nonlinear trend fuzzy granulation for carbon trading time series forecast," Applied Energy, Elsevier, vol. 352(C).
    4. Zhao, Zhenyu & Zhang, Yao & Yang, Yujia & Yuan, Shuguang, 2022. "Load forecasting via Grey Model-Least Squares Support Vector Machine model and spatial-temporal distribution of electric consumption intensity," Energy, Elsevier, vol. 255(C).
    5. Raiden Skala & Mohamed Ahmed T. A. Elgalhud & Katarina Grolinger & Syed Mir, 2023. "Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging," Energies, MDPI, vol. 16(10), pages 1-21, May.
    6. Hany Habbak & Mohamed Mahmoud & Khaled Metwally & Mostafa M. Fouda & Mohamed I. Ibrahem, 2023. "Load Forecasting Techniques and Their Applications in Smart Grids," Energies, MDPI, vol. 16(3), pages 1-33, February.
    7. Li, Ru & Tang, Bao-Jun & Yu, Biying & Liao, Hua & Zhang, Chen & Wei, Yi-Ming, 2022. "Cost-optimal operation strategy for integrating large scale of renewable energy in China’s power system: From a multi-regional perspective," Applied Energy, Elsevier, vol. 325(C).
    8. Mehmood, Faiza & Ghani, Muhammad Usman & Ghafoor, Hina & Shahzadi, Rehab & Asim, Muhammad Nabeel & Mahmood, Waqar, 2022. "EGD-SNet: A computational search engine for predicting an end-to-end machine learning pipeline for Energy Generation & Demand Forecasting," Applied Energy, Elsevier, vol. 324(C).
    9. Che, Jinxing & Yuan, Fang & Zhu, Suling & Yang, Youlong, 2022. "An adaptive ensemble framework with representative subset based weight correction for short-term forecast of peak power load," Applied Energy, Elsevier, vol. 328(C).

    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:16:y:2023:i:19:p:6921-:d:1252366. 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.