The energy storage system and power configuration of a port charging station are based on the power load of the station. The ultra-short-term load prediction of the port charging station is essential for its efficient scheduling and stable operation. To enhance the accuracy of ultra-short-term load prediction for port charging stations, a dual-model approach is proposed. This method involves modifying the results of a conventional daily load prediction model by incorporating similar-day feature data modeling. Firstly, the load data is decomposed into features using EEMD, and the fused features from the decomposition and DBN processing are inputted into the LSTM prediction model to obtain the daily load prediction results. Then, the PSO algorithm optimizes the k-density clustering parameters to cluster similar-day data. Finally, the cluster results of daily loads and the results from the similar-day model are combined to produce the final prediction. Through validation using real port data, the proposed method demonstrates an effective improvement in the accuracy of port charging station load prediction, with significant practical engineering significance.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Port Charging Station Load Prediction Method Based on EEMD-DBN-LSTM and PSO-k-Density Dual Model


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wen, Fushuan (editor) / Liu, Haoming (editor) / Wen, Huiqing (editor) / Wang, Shunli (editor) / Wang, Heng (author)

    Conference:

    International Conference on Smart Electrical Grid and Renewable Energy ; 2024 ; Suzhou, China August 09, 2024 - August 12, 2024



    Publication date :

    2025-02-28


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Train wheel size prediction based on EEMD-LSTM model

    Chen, Chunjun / Li, Yongjie | British Library Conference Proceedings | 2022


    Electric vehicle charging load prediction method based on IACO-Attention-LSTM

    HUANG LIANG / TUO LIANGYU / CUI YIBO et al. | European Patent Office | 2024

    Free access

    A modified unbiased GM(1,1) prediction model based EEMD

    Jiang, Haixu / Zhang, Ke / Wang, Jingyu et al. | IEEE | 2016


    LSTM model generation method, charging duration prediction method and medium

    TANG XURI / LI CHUNXI / WEI GAOYI | European Patent Office | 2021

    Free access

    Automobile charging station load intelligent prediction method and system

    ZHANG SHU / YU YUANHENG | European Patent Office | 2025

    Free access