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Short-Term Wind Power Prediction Based on Encoder-Decoder Network and Multi-Point Focused Linear Attention Mechanism.

Jinlong Mei1, Chengqun Wang2, Shuyun Luo2

  • 1School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)
|September 14, 2024
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Summary

Accurate wind power generation prediction is vital for grid stability. A new composite model, MLL-MPFLA, combines multilayer perceptron (MLP) and LSTM networks for improved short-term forecasting, enhancing grid security.

Keywords:
LSTM networkencoder–decoder networkmulti-point focused linear attentionshort-term wind power prediction

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Area of Science:

  • Renewable Energy Systems
  • Artificial Intelligence in Power Engineering
  • Grid Integration of Wind Power

Background:

  • Wind energy is a clean but unpredictable power source, posing challenges for grid stability.
  • Accurate short-term wind power generation forecasting is essential to mitigate grid integration risks.
  • Existing models often struggle to capture complex temporal and multidimensional features of wind power data.

Purpose of the Study:

  • To propose a novel composite model, MLL-MPFLA, for enhanced short-term wind power generation prediction.
  • To improve the accuracy and reliability of wind power forecasting for better grid management.
  • To validate the proposed model's performance against established forecasting techniques.

Main Methods:

  • A composite model (MLL-MPFLA) integrating a multilayer perceptron (MLP) for feature extraction and an LSTM-based encoder-decoder network for temporal analysis.
  • Utilization of a multi-point focused linear attention mechanism during the decoding phase to refine predictions.
  • Comparative performance evaluation against MLP, LSTM, LSTM-Attention-LSTM, LSTM-Self_Attention-LSTM, and CNN-LSTM-Attention models.

Main Results:

  • The MLL-MPFLA model demonstrated superior predictive performance across key metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R2).
  • The combination of MLP for multidimensional feature extraction and LSTM for temporal dependency exploration proved effective.
  • The multi-point focused linear attention mechanism significantly contributed to enhanced prediction accuracy.

Conclusions:

  • The proposed MLL-MPFLA model offers a significant advancement in short-term wind power generation forecasting.
  • The model's ability to integrate multidimensional and temporal features leads to more accurate predictions, crucial for grid stability.
  • MLL-MPFLA provides a robust solution for optimizing the integration of wind energy into power grids.