The accurate, efficient, and reliable forecasting of wind speed is a hot research topic in wind power generation and integration. However, available forecasting models focus on forecasting the wind speed using historical wind speed data and ignore multidimensional meteorological variables. The objective is to develop a hybrid model with multidimensional meteorological variables for forecasting the wind speed accurately. The complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is applied to handle the nonlinearity of the wind speed. Then, the original wind speed will be decomposed into a series of intrinsic model functions with specified numbers of frequencies. A quadratic model that considers the two-way interactions between factors is used to pursue accurate forecasting. To reduce the model complexity, Gram–Schmidt-based feature selection (GSFS) is applied to extract the important meteorological factors. Finally, all the forecasting values of IMFs will be summed by assigning weights that are carefully determined by the whale optimization algorithm (WOA). The proposed forecasting approach has been applied on six datasets that were collected in Qinghai province and is compared with several state-of-the-art wind speed forecasting models. The forecasting results demonstrate that the proposed model can represent the nonlinearity of the wind speed and deliver better results than the competitors.

中文翻译：

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青海地区风速多维气象预报方法

准确，高效，可靠的风速预测是风力发电与集成的研究热点。但是，可用的预测模型侧重于使用历史风速数据预测风速，而忽略了多维气象变量。目的是开发一种具有多维气象变量的混合模型，以准确预测风速。具有自适应噪声的互补集成经验模式分解（CEEMDAN）用于处理风速的非线性。然后，原始风速将分解为具有指定数量的频率的一系列固有模型函数。考虑因素之间双向交互作用的二次模型用于进行准确的预测。为了降低模型的复杂性，基于Gram–Schmidt的特征选择（GSFS）用于提取重要的气象因素。最后，通过分配由鲸鱼优化算法（WOA）仔细确定的权重，将对IMF的所有预测值求和。所提出的预测方法已应用于青海省收集的六个数据集，并与几种最新的风速预测模型进行了比较。预测结果表明，所提出的模型可以代表风速的非线性，并且比竞争对手具有更好的结果。所提出的预测方法已应用于青海省收集的六个数据集，并与几种最新的风速预测模型进行了比较。预测结果表明，所提出的模型可以代表风速的非线性，并且比竞争对手具有更好的结果。所提出的预测方法已应用于青海省收集的六个数据集，并与几种最新的风速预测模型进行了比较。预测结果表明，所提出的模型可以代表风速的非线性，并且比竞争对手具有更好的结果。