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Time-Series Prediction of Electricity Load for Charging Piles in a Region of China Based on Broad Learning System

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  • Liansong Yu

    (School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
    State Grid Electric Power Research Institute Wuhan Nanrui Co., Ltd., Wuhan 430070, China
    These authors contributed equally to this work.)

  • Xiaohu Ge

    (School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
    These authors contributed equally to this work.)

Abstract

This paper introduces a novel electricity load time-series prediction model, utilizing a broad learning system to tackle the challenge of low prediction accuracy caused by the unpredictable nature of electricity load sequences in a specific region of China. First, a correlation analysis with mutual information is utilized to identify the key factors affecting the electricity load. Second, variational mode decomposition is employed to obtain different mode information, and then a broad learning system is utilized to build a prediction model with different mode information. Finally, particle swarm optimization is used to fuse the prediction models under different modes. Simulation experiments using real data validate the efficiency of the proposed method, demonstrating that it offers higher accuracy compared to advanced modeling techniques and can assist in optimal electricity-load scheduling decision-making. Additionally, the R 2 of the proposed model is 0.9831, the P R M S E is 21.8502, the P M A E is 17.0097, and the P M A P E is 2.6468.

Suggested Citation

  • Liansong Yu & Xiaohu Ge, 2024. "Time-Series Prediction of Electricity Load for Charging Piles in a Region of China Based on Broad Learning System," Mathematics, MDPI, vol. 12(13), pages 1-12, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:13:p:2147-:d:1431219
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    References listed on IDEAS

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    1. Buzna, Luboš & De Falco, Pasquale & Ferruzzi, Gabriella & Khormali, Shahab & Proto, Daniela & Refa, Nazir & Straka, Milan & van der Poel, Gijs, 2021. "An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations," Applied Energy, Elsevier, vol. 283(C).
    2. Wang, Shuai & Ma, Hongyan & Zhang, Yingda & Li, Shengyan & He, Wei, 2023. "Remaining useful life prediction method of lithium-ion batteries is based on variational modal decomposition and deep learning integrated approach," Energy, Elsevier, vol. 282(C).
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