IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i18p4667-d1481217.html
   My bibliography  Save this article

Short-Term Electrical Load Forecasting Based on IDBO-PTCN-GRU Model

Author

Listed:
  • Renxi Gong

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Zhihuan Wei

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Yan Qin

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Tao Liu

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Jiawei Xu

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

Abstract

Accurate electrical load forecasting is crucial for the stable operation of power systems. However, existing forecasting models face limitations when handling multidimensional features and feature interactions. Additionally, traditional metaheuristic algorithms tend to become trapped in local optima during the optimization process, negatively impacting model performance and prediction accuracy. To address these challenges, this paper proposes a short-term electrical load forecasting method based on a parallel Temporal Convolutional Network–Gated Recurrent Unit (PTCN-GRU) model, optimized by an improved Dung Beetle Optimization algorithm (IDBO). This method employs a parallel TCN structure, using TCNs with different kernel sizes to extract and integrate multi-scale temporal features, thereby overcoming the limitations of traditional TCNs in processing multidimensional input data. Furthermore, this paper enhances the optimization performance and global search capability of the traditional Dung Beetle Optimization algorithm through several key improvements. Firstly, Latin hypercube sampling is introduced to increase the diversity of the initial population. Next, the Golden Sine Algorithm is integrated to refine the search behavior. Finally, a Cauchy–Gaussian mutation strategy is incorporated in the later stages of iteration to further strengthen the global search capability. Extensive experimental results demonstrate that the proposed IDBO-PTCN-GRU model significantly outperforms comparison models across all evaluation metrics. Specifically, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were reduced by 15.01%, 14.44%, and 14.42%, respectively, while the coefficient of determination (R 2 ) increased by 2.13%. This research provides a novel approach to enhancing the accuracy of electrical load forecasting.

Suggested Citation

  • Renxi Gong & Zhihuan Wei & Yan Qin & Tao Liu & Jiawei Xu, 2024. "Short-Term Electrical Load Forecasting Based on IDBO-PTCN-GRU Model," Energies, MDPI, vol. 17(18), pages 1-24, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:18:p:4667-:d:1481217
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/18/4667/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/18/4667/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiao, Liye & Shao, Wei & Liang, Tulu & Wang, Chen, 2016. "A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting," Applied Energy, Elsevier, vol. 167(C), pages 135-153.
    2. Sekhar, Charan & Dahiya, Ratna, 2023. "Robust framework based on hybrid deep learning approach for short term load forecasting of building electricity demand," Energy, Elsevier, vol. 268(C).
    3. Li, Song & Goel, Lalit & Wang, Peng, 2016. "An ensemble approach for short-term load forecasting by extreme learning machine," Applied Energy, Elsevier, vol. 170(C), pages 22-29.
    4. Hafeez, Ghulam & Khan, Imran & Jan, Sadaqat & Shah, Ibrar Ali & Khan, Farrukh Aslam & Derhab, Abdelouahid, 2021. "A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid," Applied Energy, Elsevier, vol. 299(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. Zhang, Jinliang & Siya, Wang & Zhongfu, Tan & Anli, Sun, 2023. "An improved hybrid model for short term power load prediction," Energy, Elsevier, vol. 268(C).
    2. Yuqi Dong & Xuejiao Ma & Chenchen Ma & Jianzhou Wang, 2016. "Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting," Energies, MDPI, vol. 9(12), pages 1-30, December.
    3. Wu, Zhuochun & Zhao, Xiaochen & Ma, Yuqing & Zhao, Xinyan, 2019. "A hybrid model based on modified multi-objective cuckoo search algorithm for short-term load forecasting," Applied Energy, Elsevier, vol. 237(C), pages 896-909.
    4. Hisham Alghamdi & Ghulam Hafeez & Sajjad Ali & Safeer Ullah & Muhammad Iftikhar Khan & Sadia Murawwat & Lyu-Guang Hua, 2023. "An Integrated Model of Deep Learning and Heuristic Algorithm for Load Forecasting in Smart Grid," Mathematics, MDPI, vol. 11(21), pages 1-22, November.
    5. Zhang, Jinliang & Wei, Yi-Ming & Li, Dezhi & Tan, Zhongfu & Zhou, Jianhua, 2018. "Short term electricity load forecasting using a hybrid model," Energy, Elsevier, vol. 158(C), pages 774-781.
    6. Wu, Jinran & Cui, Zhesen & Chen, Yanyan & Kong, Demeng & Wang, You-Gan, 2019. "A new hybrid model to predict the electrical load in five states of Australia," Energy, Elsevier, vol. 166(C), pages 598-609.
    7. Lin Lin & Lin Xue & Zhiqiang Hu & Nantian Huang, 2018. "Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours," Energies, MDPI, vol. 11(7), pages 1-30, July.
    8. Ihab Taleb & Guillaume Guerard & Frédéric Fauberteau & Nga Nguyen, 2022. "A Flexible Deep Learning Method for Energy Forecasting," Energies, MDPI, vol. 15(11), pages 1-16, May.
    9. Xiao, Liye & Shao, Wei & Yu, Mengxia & Ma, Jing & Jin, Congjun, 2017. "Research and application of a hybrid wavelet neural network model with the improved cuckoo search algorithm for electrical power system forecasting," Applied Energy, Elsevier, vol. 198(C), pages 203-222.
    10. 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).
    11. Andreas Lenk & Marcus Vogt & Christoph Herrmann, 2024. "An Approach to Predicting Energy Demand Within Automobile Production Using the Temporal Fusion Transformer Model," Energies, MDPI, vol. 18(1), pages 1-34, December.
    12. Qu, Zhijian & Xu, Juan & Wang, Zixiao & Chi, Rui & Liu, Hanxin, 2021. "Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method," Energy, Elsevier, vol. 227(C).
    13. Pesantez, Jorge E. & Li, Binbin & Lee, Christopher & Zhao, Zhizhen & Butala, Mark & Stillwell, Ashlynn S., 2023. "A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment," Energy, Elsevier, vol. 283(C).
    14. Cocco Mariani, Viviana & Hennings Och, Stephan & dos Santos Coelho, Leandro & Domingues, Eric, 2019. "Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models," Applied Energy, Elsevier, vol. 249(C), pages 204-221.
    15. Wu, Zhuochun & Xiao, Liye, 2019. "A structure with density-weighted active learning-based model selection strategy and meteorological analysis for wind speed vector deterministic and probabilistic forecasting," Energy, Elsevier, vol. 183(C), pages 1178-1194.
    16. Sun, Wei & Zhang, Chongchong, 2018. "Analysis and forecasting of the carbon price using multi—resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm," Applied Energy, Elsevier, vol. 231(C), pages 1354-1371.
    17. Tang, Zihan & Ji, Tianyao & Kang, Jiaxi & Huang, Yunlin & Tang, Wenhu, 2025. "Learning global and local features of power load series through transformer and 2D-CNN: An image-based multi-step forecasting approach incorporating phase space reconstruction," Applied Energy, Elsevier, vol. 378(PA).
    18. Xing Zhang & Zhuoqun Wei, 2019. "A Hybrid Model Based on Principal Component Analysis, Wavelet Transform, and Extreme Learning Machine Optimized by Bat Algorithm for Daily Solar Radiation Forecasting," Sustainability, MDPI, vol. 11(15), pages 1-20, July.
    19. Zhineng Hu & Jing Ma & Liangwei Yang & Liming Yao & Meng Pang, 2019. "Monthly electricity demand forecasting using empirical mode decomposition-based state space model," Energy & Environment, , vol. 30(7), pages 1236-1254, November.
    20. Zulfiqar, M. & Kamran, M. & Rasheed, M.B. & Alquthami, T. & Milyani, A.H., 2023. "A hybrid framework for short term load forecasting with a navel feature engineering and adaptive grasshopper optimization in smart grid," Applied Energy, Elsevier, vol. 338(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:17:y:2024:i:18:p:4667-:d:1481217. 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.