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Ultra-Short-Term Wind Power Forecasting Based on the MSADBO-LSTM Model

Author

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  • Ziquan Zhao

    (School of Electrical and Information Engineering, Beihua University, Jilin 132013, China)

  • Jing Bai

    (School of Electrical and Information Engineering, Beihua University, Jilin 132013, China)

Abstract

To address the challenges of the strong randomness and intermittency of wind power generation that affect wind power grid integration, power system scheduling, and the safe and stable operation of the system, an improved Dung Beetle Optimization Algorithm (MSADBO) is proposed to optimize the hyperparameters of the Long Short-Term Memory neural network (LSTM) for ultra-short-term wind power forecasting. By applying Bernoulli mapping for population initialization, the model’s sensitivity to wind power fluctuations is reduced, which accelerates the algorithm’s convergence speed. Incorporating an improved Sine Algorithm (MSA) into the forecasting model for this nonlinear problem significantly improves the position update strategy of the Dung Beetle Optimization Algorithm (DBO), which tends to be overly random and prone to local optima. This enhancement boosts the algorithm’s exploration capabilities both locally and globally, improving the rapid responsiveness of ultra-short-term wind power forecasting. Furthermore, an adaptive Gaussian–Cauchy mixture perturbation is introduced to interfere with individuals, increasing population diversity, escaping local optima, and enabling the continued exploration of other areas of the solution space until the global optimum is ultimately found. By optimizing three hyperparameters of the LSTM using the MSADBO algorithm, the prediction accuracy of the model is greatly enhanced. After simulation validation, taking winter as an example, the MSADBO-LSTM predictive model achieved a reduction in the MAE metric of 40.6% compared to LSTM, 20.12% compared to PSO-LSTM, and 3.82% compared to DBO-LSTM. The MSE decreased by 45.4% compared to LSTM, 40.78% compared to PSO-LSTM, and 16.62% compared to DBO-LSTM. The RMSE was reduced by 26.11% compared to LSTM, 23.05% compared to PSO-LSTM, and 8.69% compared to DBO-LSTM. Finally, the MAPE declined by 79.83% compared to LSTM, 31.88% compared to PSO-LSTM, and 29.62% compared to DBO-LSTM. This indicates that the predictive model can effectively enhance the accuracy of wind power forecasting.

Suggested Citation

  • Ziquan Zhao & Jing Bai, 2024. "Ultra-Short-Term Wind Power Forecasting Based on the MSADBO-LSTM Model," Energies, MDPI, vol. 17(22), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5689-:d:1520609
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    References listed on IDEAS

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    1. Akram Belazi & Héctor Migallón & Daniel Gónzalez-Sánchez & Jorge Gónzalez-García & Antonio Jimeno-Morenilla & José-Luis Sánchez-Romero, 2022. "Enhanced Parallel Sine Cosine Algorithm for Constrained and Unconstrained Optimization," Mathematics, MDPI, vol. 10(7), pages 1-47, April.
    2. Kamil Szostek & Damian Mazur & Grzegorz Drałus & Jacek Kusznier, 2024. "Analysis of the Effectiveness of ARIMA, SARIMA, and SVR Models in Time Series Forecasting: A Case Study of Wind Farm Energy Production," Energies, MDPI, vol. 17(19), pages 1-18, September.
    3. Yin, Hao & Ou, Zuhong & Huang, Shengquan & Meng, Anbo, 2019. "A cascaded deep learning wind power prediction approach based on a two-layer of mode decomposition," Energy, Elsevier, vol. 189(C).
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    Cited by:

    1. Yan Chen & Miaolin Yu & Haochong Wei & Huanxing Qi & Yiming Qin & Xiaochun Hu & Rongxing Jiang, 2025. "A Lightweight Framework for Rapid Response to Short-Term Forecasting of Wind Farms Using Dual Scale Modeling and Normalized Feature Learning," Energies, MDPI, vol. 18(3), pages 1-20, January.
    2. Na Fang & Zhengguang Liu & Shilei Fan, 2025. "Short-Term Wind Power Prediction Method Based on CEEMDAN-VMD-GRU Hybrid Model," Energies, MDPI, vol. 18(6), pages 1-18, March.

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