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Day-Ahead Electricity Price Prediction and Error Correction Method Based on Feature Construction–Singular Spectrum Analysis–Long Short-Term Memory

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  • Yuzhe Jiang

    (School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China)

  • Weiqing Sun

    (School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China)

Abstract

Conducting electricity price prediction research has significance for the operation of the generation and transmission sides, and can guide the planning of electricity consumption. In order to further improve prediction accuracy, this paper constructs new feature based on publicly available market data, and uses feature filtering to find the feature data with the highest correlation with electricity prices in publicly available market data as input features. A model combining feature construction (FC), singular spectrum analysis (SSA), and LSTM is used for electricity price prediction. Compared with traditional LSTM models, this model reduced the MAE by 10.0, MAPE by 16.4%, and RMSE by 19.7 in the test set. This paper also proposes an error correction method for recursive prediction based on the error distribution in training and testing sets to reduce the influence of accumulated errors. The results show that the MAPE decreased by 6.1% in recursive prediction, proving that the model has good performance in prediction. By accurately predicting electricity prices and analyzing possible error ranges, the prediction method proposed in this article can better guide market participants in making decisions.

Suggested Citation

  • Yuzhe Jiang & Weiqing Sun, 2025. "Day-Ahead Electricity Price Prediction and Error Correction Method Based on Feature Construction–Singular Spectrum Analysis–Long Short-Term Memory," Energies, MDPI, vol. 18(4), pages 1-22, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:919-:d:1591347
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    References listed on IDEAS

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