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A Quantile Regression Random Forest-Based Short-Term Load Probabilistic Forecasting Method

Author

Listed:
  • Sanlei Dang

    (Meteorology Center of Guangdong Power Grid Co. Ltd., Guangzhou 510600, China)

  • Long Peng

    (Meteorology Center of Guangdong Power Grid Co. Ltd., Guangzhou 510600, China)

  • Jingming Zhao

    (Meteorology Center of Guangdong Power Grid Co. Ltd., Guangzhou 510600, China)

  • Jiajie Li

    (Meteorology Center of Guangdong Power Grid Co. Ltd., Guangzhou 510600, China)

  • Zhengmin Kong

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

Abstract

In this paper, a novel short-term load forecasting method amalgamated with quantile regression random forest is proposed. Comprised with point forecasting, it is capable of quantifying the uncertainty of power load. Firstly, a bespoke 2D data preprocessing taking advantage of empirical mode decomposition (EMD) is presented. It can effectively assist subsequent point forecasting models to extract spatial features hidden in the 2D load matrix. Secondly, by exploiting multimodal deep neural networks (DNN), three short-term load point forecasting models are conceived. Furthermore, a tailor-made multimodal spatial–temporal feature extraction is proposed, which integrates spatial features, time information, load, and electricity price to obtain more covert features. Thirdly, relying on quantile regression random forest, the probabilistic forecasting method is proposed, which exploits the results from the above three short-term load point forecasting models. Lastly, the experimental results demonstrate that the proposed method outperforms its conventional counterparts.

Suggested Citation

  • Sanlei Dang & Long Peng & Jingming Zhao & Jiajie Li & Zhengmin Kong, 2022. "A Quantile Regression Random Forest-Based Short-Term Load Probabilistic Forecasting Method," Energies, MDPI, vol. 15(2), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:2:p:663-:d:726882
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    References listed on IDEAS

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    1. Wang, Yi & Gan, Dahua & Sun, Mingyang & Zhang, Ning & Lu, Zongxiang & Kang, Chongqing, 2019. "Probabilistic individual load forecasting using pinball loss guided LSTM," Applied Energy, Elsevier, vol. 235(C), pages 10-20.
    2. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
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    1. Fábio Antônio do Nascimento Setúbal & Sérgio de Souza Custódio Filho & Newton Sure Soeiro & Alexandre Luiz Amarante Mesquita & Marcus Vinicius Alves Nunes, 2022. "Force Identification from Vibration Data by Response Surface and Random Forest Regression Algorithms," Energies, MDPI, vol. 15(10), pages 1-15, May.

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