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In order to improve the prediction accuracy of ultra short-term wind power, a combined wind power prediction model based on IEWT-FE-BO-LSTM was proposed. Firstly, an improved empirical wavelet decomposition (IEWT) was used to decompose the historical wind power data. The Fuzzy Entropy (FE) algorithm was introduced to calculate the complexity of each decomposed submodel and reconstruct the submodel. For each recostructed component, a prediction model based on long short term neural network (LSTM) was established. Bayesian optimization algorithm (BO) was used for hyper parameters to solve the problem of poor training results caused by manual parameter adjustment. The example analysis based on historical wind farm data shows that the IEWT-FE-BO-LSTM model has high prediction accuracy and efficiency for ultra short-term wind power.

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为提高超短期风功率预测精度，提出一种基于IEWT-FE-BO-LSTM的组合风功率预测模型，首先利用改进经验小波分解（IEWT）对历史风功率数据进行分解；然后引入模糊熵（FE）算法对各分解子模态进行复杂度计算重组子模态；再对各个重组分量分别建立基于长短时神经网络（LSTM）的预测模型，利用贝叶斯优化算法（BO）进行超参数组合，解决人为调参导致训练结果不佳的问题；最后通过历史风电场数据进行算例分析。结果表明，IEWT-FE-BO-LSTM模型对超短期风功率有较高的预测精度和预测效率。

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陆秋贤（1998-），男，硕士研究生，研究方向为新能源接入下机组组合方法，E-mail：1826375641@qq.com

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陆秋贤（1998-），男，硕士研究生，研究方向为新能源接入下机组组合方法，E-mail：1826375641@qq.com

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