IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v377y2025ipbs0306261924016295.html
   My bibliography  Save this article

A power load forecasting method in port based on VMD-ICSS-hybrid neural network

Author

Listed:
  • Ma, Kai
  • Nie, Xuefeng
  • Yang, Jie
  • Zha, Linlin
  • Li, Guoqiang
  • Li, Haibin

Abstract

Aiming at the problem of load fluctuation at the power end of large ports, we propose a hybrid neural network joint model based on Mode Decomposition (MD) and Change Point Detection (CPD) to accomplish the load forecasting. In this study, a two-stage joint prediction model is constructed. First, the number of Intrinsic Mode Functions (IMFs) in the Variational Mode Decomposition (VMD) process was dynamically adjusted by introducing an improved Signal Energy (SE) evaluation metric. Subsequently, a Bidirectional Gated Recurrent Unit (Bi-GRU) network is employed to predict these IMFs, and the potential effect of the breakpoints on the prediction outcomes is investigated using the Iterative Cumulative Sum of Squares (ICSS) method. Finally, the eigenmode functions are summed and reconstructed, and then combined with the breakpoint data as inputs for the second stage prediction. To ensure the efficiency of the second stage prediction, the Mogrifier Long-and Short-Term Memory (Mogrifier-LSTM) network structure is improved. In the two-stage model, the adaptive tuning of hyperparameters is implemented by a Hunter-Prey Optimization (HPO) algorithm based on a redesigned chaotic mapping strategy. During the simulation, various neural network topologies were employed to confirm the effectiveness of the model in port power load forecasting.

Suggested Citation

  • Ma, Kai & Nie, Xuefeng & Yang, Jie & Zha, Linlin & Li, Guoqiang & Li, Haibin, 2025. "A power load forecasting method in port based on VMD-ICSS-hybrid neural network," Applied Energy, Elsevier, vol. 377(PB).
    • Handle: RePEc:eee:appene:v:377:y:2025:i:pb:s0306261924016295
    DOI: 10.1016/j.apenergy.2024.124246
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924016295
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.124246?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Lucía Morales & Bernadette Andreosso-O’Callaghan, 2014. "Volatility analysis of precious metals returns and oil returns: An ICSS approach," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 38(3), pages 492-517, July.
    2. 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).
    3. Lai, Ching-Ming & Teh, Jiashen, 2022. "Network topology optimisation based on dynamic thermal rating and battery storage systems for improved wind penetration and reliability," Applied Energy, Elsevier, vol. 305(C).
    4. Wang, Kai & Hua, Yu & Huang, Lianzhong & Guo, Xin & Liu, Xing & Ma, Zhongmin & Ma, Ranqi & Jiang, Xiaoli, 2023. "A novel GA-LSTM-based prediction method of ship energy usage based on the characteristics analysis of operational data," Energy, Elsevier, vol. 282(C).
    5. Hamed, Mohammad M. & Ali, Hesham & Abdelal, Qasem, 2022. "Forecasting annual electric power consumption using a random parameters model with heterogeneity in means and variances," Energy, Elsevier, vol. 255(C).
    6. Wan, Anping & Chang, Qing & AL-Bukhaiti, Khalil & He, Jiabo, 2023. "Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism," Energy, Elsevier, vol. 282(C).
    7. Yu, Binbin & Li, Jianjing & Liu, Che & Sun, Bo, 2022. "A novel short-term electrical load forecasting framework with intelligent feature engineering," Applied Energy, Elsevier, vol. 327(C).
    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. Gou, Xiaoyi & Mi, Chuanmin & Zeng, Bo, 2025. "Mixed-frequency grey prediction model with fractional lags for electricity demand and estimation of coal power phase-out scale," Energy, Elsevier, vol. 320(C).
    2. Wu, Bizhi & Xiao, Jiangwen & Wang, Shanlin & Zhang, Ziyuan & Wen, Renqiang, 2025. "Enhancing short-term net load forecasting with additive neural decomposition and Weibull Attention," Energy, Elsevier, vol. 322(C).
    3. Wenjie Guo & Jie Liu & Jun Ma & Zheng Lan, 2025. "Short-Term Power Load Forecasting Using Adaptive Mode Decomposition and Improved Least Squares Support Vector Machine," Energies, MDPI, vol. 18(10), pages 1-17, May.

    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. Yang, Weijia & Sparrow, Sarah N. & Wallom, David C.H., 2024. "A comparative climate-resilient energy design: Wildfire Resilient Load Forecasting Model using multi-factor deep learning methods," Applied Energy, Elsevier, vol. 368(C).
    2. Hu, Rong & Zhou, Kaile & Lu, Xinhui, 2025. "Integrated loads forecasting with absence of crucial factors," Energy, Elsevier, vol. 322(C).
    3. Xu, Huifeng & Hu, Feihu & Liang, Xinhao & Zhao, Guoqing & Abugunmi, Mohammad, 2024. "A framework for electricity load forecasting based on attention mechanism time series depthwise separable convolutional neural network," Energy, Elsevier, vol. 299(C).
    4. Cui, Xuyang & Zhu, Junda & Jia, Lifu & Wang, Jiahui & Wu, Yusen, 2024. "A novel heat load prediction model of district heating system based on hybrid whale optimization algorithm (WOA) and CNN-LSTM with attention mechanism," Energy, Elsevier, vol. 312(C).
    5. Hamed, Mohammad M. & Mohammed, Ali & Olabi, Abdul Ghani, 2023. "Renewable energy adoption decisions in Jordan's industrial sector: Statistical analysis with unobserved heterogeneity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    6. Yao Zhong & Tengbin Li & Krzysztof Przystupa & Cong Lin & Guangrun Yang & Sen Yang & Orest Kochan & Jarosław Sikora, 2024. "Spatiotemporal Correlation Analysis for Predicting Current Transformer Errors in Smart Grids," Energies, MDPI, vol. 17(7), pages 1-14, March.
    7. Zhang, Huan & Liu, Tao & Liu, Wang & Zhou, Jianzhao & Zhang, Quanguo & Ren, Jingzheng, 2025. "An interpretable deep learning framework for photofermentation biological hydrogen production and process optimization," Energy, Elsevier, vol. 322(C).
    8. Wu, Long & Yin, Xunyuan & Pan, Lei & Liu, Jinfeng, 2023. "Distributed economic predictive control of integrated energy systems for enhanced synergy and grid response: A decomposition and cooperation strategy," Applied Energy, Elsevier, vol. 349(C).
    9. Zhao, Xiaoyu & Duan, Pengfei & Cao, Xiaodong & Xue, Qingwen & Zhao, Bingxu & Hu, Jinxue & Zhang, Chenyang & Yuan, Xiaoyang, 2025. "A probabilistic load forecasting method for multi-energy loads based on inflection point optimization and integrated feature screening," Energy, Elsevier, vol. 327(C).
    10. Chen, Bowen & Lin, Yonggang & Gu, Yajing & Feng, Xiangheng & Cao, Zhongpeng & Sun, Yong, 2025. "A novel active wake control strategy based on LiDAR for wind farms," Energy, Elsevier, vol. 317(C).
    11. Gomez, William & Wang, Fu-Kwun & Lo, Shih-Che, 2024. "A hybrid approach based machine learning models in electricity markets," Energy, Elsevier, vol. 289(C).
    12. Gong, Bin & An, Aimin & Shi, Yaoke & Guan, Haijiao & Jia, Wenchao & Yang, Fazhi, 2024. "An interpretable hybrid spatiotemporal fusion method for ultra-short-term photovoltaic power prediction," Energy, Elsevier, vol. 308(C).
    13. Taorong Jia & Lixiao Yao & Guoqing Yang & Qi He, 2022. "A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU," Sustainability, MDPI, vol. 14(24), pages 1-18, December.
    14. Fengtian Chang & Guanghui Zhou & Kai Ding & Jintao Li & Yanzhen Jing & Jizhuang Hui & Chao Zhang, 2023. "A CNN-LSTM and Attention-Mechanism-Based Resistance Spot Welding Quality Online Detection Method for Automotive Bodies," Mathematics, MDPI, vol. 11(22), pages 1-19, November.
    15. Duan, Tianyao & Guo, Huan & Qi, Xiao & Sun, Ming & Forrest, Jeffrey, 2024. "A novel information enhanced Grey Lotka–Volterra model driven by system mechanism and data for energy forecasting of WEET project in China," Energy, Elsevier, vol. 304(C).
    16. Moreno, Sinvaldo Rodrigues & Seman, Laio Oriel & Stefenon, Stefano Frizzo & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2024. "Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition," Energy, Elsevier, vol. 292(C).
    17. 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.
    18. Han, Peixiu & Liu, Zhongbo & Li, Chi & Sun, Zhuo & Yan, Chunxin, 2024. "A novel federated learning-based two-stage approach for ship energy consumption optimization considering both shipping data security and statistical heterogeneity," Energy, Elsevier, vol. 309(C).
    19. Wang, Danhao & Peng, Daogang & Huang, Dongmei & Zhao, Huirong & Qu, Bogang, 2025. "MMEMformer: A multi-scale memory-enhanced transformer framework for short-term load forecasting in integrated energy systems," Energy, Elsevier, vol. 322(C).
    20. Minan Tang & Changyou Wang & Jiandong Qiu & Hanting Li & Xi Guo & Wenxin Sheng, 2024. "Short-Term Load Forecasting of Electric Vehicle Charging Stations Accounting for Multifactor IDBO Hybrid Models," Energies, MDPI, vol. 17(12), pages 1-19, June.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:appene:v:377:y:2025:i:pb:s0306261924016295. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.