Wind speed Forecasting is the first step to integrate wind power into the main grid. It is important to improve the accuracy of wind speed forecasting to improve the load management side and the renewable energy integration. Due to the chaotic in the wind speed fluctuation the wind speed data forecasting is difficult. Many models are proposed in the literature for wind speed forecasting. This paper is proposing accurate hybrid models for wind speed forecasting to improve the overall system accuracy. These hybrid models involve various combinations of Wavelet and Artificial Neural Network (WNN and ANN), Time Series (TS) and Recurrent Kalman Filter (RKF). Three main hybrid models are proposed and tested. From those three models the best model with the highest performance is the hybrid of WNN, RKF, TS. The order of the techniques used in the hybrid models is very important. Different combinations with different orders are tested in this stage. Different models are tested with different techniques order. The proposed work is validated by using different unseen dataset with the proposed models and prove their effectiveness. All proposed models are accurate, but the best model is a hybrid of WNN, TS and RKF in sequence.

风速预测是将风能整合到主电网中的第一步。重要的是提高风速预测的准确性，以改善负荷管理方面和可再生能源的整合。由于风速波动的混乱，因此很难预测风速数据。在文献中提出了许多用于风速预测的模型。本文提出了用于风速预测的精确混合模型，以提高整体系统的准确性。这些混合模型涉及小波和人工神经网络（WNN和ANN），时间序列（TS）和递归卡尔曼滤波器（RKF）的各种组合。提出并测试了三种主要的混合模型。从这三个模型中，性能最高的最佳模型是WNN，RKF，TS的混合体。混合模型中使用的技术的顺序非常重要。在此阶段测试具有不同顺序的不同组合。使用不同的技术顺序测试不同的模型。通过使用不同的看不见的数据集和提出的模型来验证提出的工作，并证明其有效性。所有提出的模型都是准确的，但是最好的模型是WNN，TS和RKF的顺序混合。