Accurate wind power forecasting helps to carry out effective scheduling and scientific management of wind power, and improve the security and reliability of the power grid. However, the intermittency, volatility and instability of wind energy make wind power forecasting challenging. Therefore, in order to improve the accuracy and stability of wind power forecasting, this paper proposes a bidirectional gated recurrent unit (BiGRU) multi-step wind power forecasting approach based on multi-label integration random forest (MLRF) feature selection and neural network clustering (NNClustering). The proposed MLRF method extends the applicability of random forest through multiple criteria and enables feature selection for multi-step forecasting tasks of multi-factor time series to obtain optimal input features and time steps, which reduces the computational cost and improves the generalization ability of the model. The proposed NNClustering method establishes a novel convolution-based clustering structure and adjusts the parameters by gradient descent method to obtain the optimal clustering centers, and the robust data applicability of the method is validated in multiple seasonal experiments. The WOA-BiGRU forecasting model is constructed separately for each cluster, which reduces the modeling difficulty and better extracts the characteristics. The BiGRU model extracts more efficient characteristics by processing sequences in both directions and the important parameters of BiGRU are optimized by the whale optimization algorithm (WOA) to obtain the optimal forecasting model. Experimental results over multiple seasons show that the proposed hybrid approach has good forecasting performance and robustness, which provides a novel and efficient solution for wind power forecasting.

中文翻译：

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基于多标签集成随机森林特征选择和神经网络聚类的新型BiGRU多步风电预测方法


准确的风电功率预测有助于对风电进行有效调度和科学管理，提高电网的安全可靠性。然而，风能的间歇性、波动性和不稳定性给风电预测带来了挑战。因此，为了提高风电功率预测的准确性和稳定性，提出一种基于多标签集成随机森林（MLRF）特征选择和神经网络聚类的双向门控循环单元（BiGRU）多步风电功率预测方法（神经网络聚类）。所提出的MLRF方法通过多准则扩展了随机森林的适用性，并能够对多因素时间序列的多步预测任务进行特征选择，以获得最优的输入特征和时间步，从而降低了计算成本并提高了泛化能力模型。所提出的NNClustering方法建立了一种新颖的基于卷积的聚类结构，并通过梯度下降方法调整参数以获得最佳聚类中心，并在多个季节实验中验证了该方法的鲁棒数据适用性。 WOA-BiGRU预测模型针对每个聚类单独构建，降低了建模难度，更好地提取特征。 BiGRU模型通过处理双向序列来提取更高效的特征，并通过鲸鱼优化算法（WOA）对BiGRU的重要参数进行优化，以获得最优的预测模型。多个季节的实验结果表明，所提出的混合方法具有良好的预测性能和鲁棒性，为风电功率预测提供了一种新颖有效的解决方案。