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    Short-term electrical load forecasting model based on multi-dimensional meteorological information spatio-temporal fusion and optimized variational mode decomposition

    95902.pdf (4.639Mb)
    Access Status
    Open access
    Authors
    Wang, L.
    Zhou, X.
    Xu, Honglei
    Tian, T.
    Tong, H.
    Date
    2023
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Wang, L. and Zhou, X. and Xu, H. and Tian, T. and Tong, H. 2023. Short-term electrical load forecasting model based on multi-dimensional meteorological information spatio-temporal fusion and optimized variational mode decomposition. IET Generation, Transmission and Distribution. 17 (20): pp. 4647-4663.
    Source Title
    IET Generation, Transmission and Distribution
    DOI
    10.1049/gtd2.12992
    Additional URLs
    https://creativecommons.org/licenses/by-nc-nd/4.0/
    ISSN
    1751-8687
    Faculty
    Faculty of Science and Engineering
    School
    School of Elec Eng, Comp and Math Sci (EECMS)
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/LP160100528
    URI
    http://hdl.handle.net/20.500.11937/96138
    Collection
    • Curtin Research Publications
    Abstract

    This paper proposes a method to enhance the accuracy of power load forecasting by considering the variability in the impact of multi-dimensional meteorological information on power load in diverse regions. The proposed method employs spatio-temporal fusion (SF) of multi-dimensional meteorological information and applies the Copula theory to analyze the non-linear coupling of meteorological information from multiple stations with power load to achieve SF in the spatial dimension. To enhance the accuracy of load forecasting in the time dimension, this paper improves the core parameters of the variational mode decomposition (VMD) using the marine predators algorithm (MPA) and utilizes the weighted permutation entropy (WPE) to construct the MPA-VMD fitness function for the adaptive decomposition of the load sequence. Moreover, this paper constructs input sets for the long short-term memory model and the MPA-LSSVM model by combining each component of the time dimension and each meteorological information of the spatial dimension to obtain the prediction results of each component. The prediction model corresponding to each component is selected according to the evaluation index and reconstructed to obtain the overall prediction results. The analysis results demonstrate that the proposed forecasting method outperforms the traditional forecasting method and effectively enhances the accuracy of power load forecasting.

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