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Ultra Short-Term Power Load Forecasting Based on Similar Day Clustering and Ensemble Empirical Mode Decomposition

Author

Listed:
  • Wenhui Zeng

    (School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China)

  • Jiarui Li

    (Huadian Electric Power Research Institute Co., Ltd., Hangzhou 310030, China)

  • Changchun Sun

    (Huadian Electric Power Research Institute Co., Ltd., Hangzhou 310030, China)

  • Lin Cao

    (School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China)

  • Xiaoping Tang

    (Huadian Electric Power Research Institute Co., Ltd., Hangzhou 310030, China)

  • Shaolong Shu

    (School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China)

  • Junsheng Zheng

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

Abstract

With the increasing demand of the power industry for load forecasting, improving the accuracy of power load forecasting has become increasingly important. In this paper, we propose an ultra short-term power load forecasting method based on similar day clustering and EEMD (Ensemble Empirical Mode Decomposition). In detail, the K-means clustering algorithm was utilized to divide the historical data into different clusters. Through EEMD, the load data of each cluster were decomposed into several sub-sequences with different time scales. The LSTNet (Long- and Short-term Time-series Network) was adopted as the load forecasting model for these sub-sequences. The forecast results for different sub-sequences were combined as the expected result. The proposed method predicts the load in the next 4 h with an interval of 15 min. The experimental results show that the proposed method obtains higher prediction accuracy than other comparable forecasting models.

Suggested Citation

  • Wenhui Zeng & Jiarui Li & Changchun Sun & Lin Cao & Xiaoping Tang & Shaolong Shu & Junsheng Zheng, 2023. "Ultra Short-Term Power Load Forecasting Based on Similar Day Clustering and Ensemble Empirical Mode Decomposition," Energies, MDPI, vol. 16(4), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1989-:d:1071438
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    References listed on IDEAS

    as
    1. Gamze Nalcaci & Ayse Özmen & Gerhard Wilhelm Weber, 2019. "Long-term load forecasting: models based on MARS, ANN and LR methods," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 27(4), pages 1033-1049, December.
    2. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
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    Cited by:

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    2. Zheng, Peijun & Zhou, Heng & Liu, Jiang & Nakanishi, Yosuke, 2023. "Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture," Applied Energy, Elsevier, vol. 349(C).

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