Accurate wind speed prediction is of great importance for the operation of wind farms, and extensive efforts have been made to develop effective forecasting methods in this regard. However, the feature selection of data input as well as optimization of deep learning models have received comparatively less attention, leading to unreliable forecasting results. This research proposes a novel hybrid model that integrates data preprocessing, feature selection, and optimized forecasting for improved wind speed prediction. Specifically, a powerful preprocessing technique is utilized to reduce data noise disturbances, while an innovative two‐stage feature selection is designed to achieve the optimal input data format for forecasting purposes. Moreover, a hybrid forecasting module based on long‐short term memory, which is optimized by the Bayesian optimization algorithm, has been developed to enhance the efficiency and reliability of the model. The empirical study used 10‐min interval wind speed data of four seasons for presentation and evaluation results demonstrated its superior performance in effectively learning the volatility and irregularity features of wind speed series, which established a solid foundation for practical applications in wind power systems.

中文翻译：

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一种用于风速研究的具有特征选择和深度学习的新型混合预测模型

准确的风速预测对于风电场的运行至关重要，为此，人们在开发有效的预测方法方面做出了广泛的努力。然而，数据输入的特征选择以及深度学习模型的优化受到的关注相对较少，导致预测结果不可靠。这项研究提出了一种新颖的混合模型，该模型集成了数据预处理、特征选择和优化预测，以改进风速预测。具体来说，利用强大的预处理技术来减少数据噪声干扰，同时设计创新的两阶段特征选择来实现用于预测目的的最佳输入数据格式。此外，还开发了基于长短期记忆的混合预测模块，并通过贝叶斯优化算法进行优化，以提高模型的效率和可靠性。实证研究利用四个季节10 min间隔的风速数据进行呈现和评估，结果表明其在有效学习风速序列的波动性和不规则性特征方面具有优越的性能，为风电系统的实际应用奠定了坚实的基础。