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Extreme Gradient Boosting Model for Day-Ahead STLF in National Level Power System: Estonia Case Study

Author

Listed:
  • Qinghe Zhao

    (Electrical Engineering and Information College, Northeast Agricultural University, Harbin 150030, China)

  • Xinyi Liu

    (Electrical Engineering and Information College, Northeast Agricultural University, Harbin 150030, China)

  • Junlong Fang

    (Electrical Engineering and Information College, Northeast Agricultural University, Harbin 150030, China)

Abstract

Short-term power load forecasting refers to the use of load and weather information to forecast the Day-ahead load, which is very important for power dispatch and the establishment of the power spot market. In this manuscript, a comprehensive study on the frame of input data for electricity load forecasting is proposed based on the extreme gradient boosting algorithm. Periodicity was the first of the historical load data to be analyzed using discrete Fourier transform, autocorrelation function, and partial autocorrelation function to determine the key width of a sliding window for an optimization load feature. The mean absolute error (MAE) of the frame reached 52.04 using a boosting model with a 7-day width in the validation dataset. Second, the fusing of datetime variables and meteorological information factors was discussed in detail and determined how to best improve performance. The datetime variables were determined as a form of integer, sine–cosine pairs, and Boolean-type combinations, and the meteorological features were determined as a combination with 540 features from 15 sampled sites, which further decreased MAE to 44.32 in the validation dataset. Last, a training method for day-ahead forecasting was proposed to combine the Minkowski distance to determine the historical span. Under this framework, the performance has been significantly improved without any tuning for the boosting algorithm. The proposed method further decreased MAE to 37.84. Finally, the effectiveness of the proposed method is evaluated using a 200-day load dataset from the Estonian grid. The achieved MAE of 41.69 outperforms other baseline models, with MAE ranging from 65.03 to 104.05. This represents a significant improvement of 35.89% over the method currently employed by the European Network of Transmission System Operators for Electricity (ENTSO-E). The robustness of the proposal method can be also guaranteed with excellent performance in extreme weather and on special days.

Suggested Citation

  • Qinghe Zhao & Xinyi Liu & Junlong Fang, 2023. "Extreme Gradient Boosting Model for Day-Ahead STLF in National Level Power System: Estonia Case Study," Energies, MDPI, vol. 16(24), pages 1-29, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:7962-:d:1296449
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    References listed on IDEAS

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    1. Hirth, Lion & Mühlenpfordt, Jonathan & Bulkeley, Marisa, 2018. "The ENTSO-E Transparency Platform – A review of Europe’s most ambitious electricity data platform," Applied Energy, Elsevier, vol. 225(C), pages 1054-1067.
    2. Tao Hong & Pierre Pinson & Yi Wang & Rafal Weron & Dazhi Yang & Hamidreza Zareipour, 2020. "Energy forecasting: A review and outlook," WORking papers in Management Science (WORMS) WORMS/20/08, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    3. Massaoudi, Mohamed & Refaat, Shady S. & Chihi, Ines & Trabelsi, Mohamed & Oueslati, Fakhreddine S. & Abu-Rub, Haitham, 2021. "A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting," Energy, Elsevier, vol. 214(C).
    4. Morais, Lucas Barros Scianni & Aquila, Giancarlo & de Faria, Victor Augusto Durães & Lima, Luana Medeiros Marangon & Lima, José Wanderley Marangon & de Queiroz, Anderson Rodrigo, 2023. "Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system," Applied Energy, Elsevier, vol. 348(C).
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