One of the most important preconditions for guaranteeing a smooth link between wind farms and the power system is to develop an accurate model for forecasting the wind speed. This paper describes a novel wind speed prediction model based on dynamic adaptive variable-weight optimization theory that considers the relevance of historical observations. The model applies signal preprocessing to recorded wind speed observations using ensemble empirical mode decomposition. The decomposed signals are then subjected to a random noise reduction procedure, which improves the robustness of the prediction model. An autoregressive integrated moving average model, general regression neural network, and long short-term memory are used to recognize the different features of each decomposed subsequence. Brain storm optimization is then applied to further promote the forecasting performance by integrating different forecasting models with dynamically adapted variable weights. To evaluate the prediction capacity of the proposed method, three case studies are conducted. The experimental outcomes reveal that the method presented in this paper provides more satisfactory prediction ability and robustness than other models.One of the most important preconditions for guaranteeing a smooth link between wind farms and the power system is to develop an accurate model for forecasting the wind speed. This paper describes a novel wind speed prediction model based on dynamic adaptive variable-weight optimization theory that considers the relevance of historical observations. The model applies signal preprocessing to recorded wind speed observations using ensemble empirical mode decomposition. The decomposed signals are then subjected to a random noise reduction procedure, which improves the robustness of the prediction model. An autoregressive integrated moving average model, general regression neural network, and long short-term memory are used to recognize the different features of each decomposed subsequence. Brain storm optimization is then applied to further promote the forecasting performance by integrating different forecasting models with dynamically adapted variable weights. To evaluate the prediction capacity of the propo...

中文翻译：

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基于动态自适应变权优化的短期风速预测混合新方法

确保风电场与电力系统之间顺畅连接的最重要前提之一是开发准确的风速预测模型。本文描述了一种基于动态自适应变权优化理论的新型风速预测模型，该模型考虑了历史观测的相关性。该模型使用集合经验模式分解将信号预处理应用于记录的风速观测。然后对分解后的信号进行随机降噪过程，从而提高预测模型的鲁棒性。使用自回归集成移动平均模型、通用回归神经网络和长短期记忆来识别每个分解子序列的不同特征。然后通过将不同的预测模型与动态适应的可变权重相结合，应用头脑风暴优化来进一步提升预测性能。为了评估所提出方法的预测能力，进行了三个案例研究。实验结果表明，本文提出的方法提供了比其他模型更令人满意的预测能力和鲁棒性。保证风电场和电力系统之间顺畅连接的最重要前提之一是开发准确的风预测模型。速度。本文描述了一种基于动态自适应变权优化理论的新型风速预测模型，该模型考虑了历史观测的相关性。该模型使用集合经验模式分解将信号预处理应用于记录的风速观测。然后对分解后的信号进行随机降噪过程，从而提高预测模型的鲁棒性。使用自回归集成移动平均模型、通用回归神经网络和长短期记忆来识别每个分解子序列的不同特征。然后通过将不同的预测模型与动态适应的可变权重相结合，应用头脑风暴优化来进一步提升预测性能。评估提案的预测能力... 使用自回归集成移动平均模型、通用回归神经网络和长短期记忆来识别每个分解子序列的不同特征。然后通过将不同的预测模型与动态适应的可变权重相结合，应用头脑风暴优化来进一步提升预测性能。评估提案的预测能力... 使用自回归集成移动平均模型、通用回归神经网络和长短期记忆来识别每个分解子序列的不同特征。然后通过将不同的预测模型与动态适应的可变权重相结合，应用头脑风暴优化来进一步提升预测性能。评估提案的预测能力...