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首页> 外文期刊>Sustainable Energy Technologies and Assessments >An intelligent hybrid model of neuro Wavelet, time series and Recurrent Kalman Filter for wind speed forecasting
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An intelligent hybrid model of neuro Wavelet, time series and Recurrent Kalman Filter for wind speed forecasting

机译：用于风速预测的神经小波，时间序列和经常性卡尔曼滤波器的智能混合模型

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摘要

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.
机译：风速预测是将风力集成到主电网中的第一步。重要的是提高风速预测的准确性，以改善负荷管理方和可再生能源集成。由于风速波动中的混乱，风速数据预测难。在风速预测文献中提出了许多模型。本文提出了用于风速预测的精确混合模型，以提高整体系统精度。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）和经常性卡尔曼滤波器（RKF）的各种组合。提出并测试了三种主要混合动力模型。从这三种模型，最高性能的最佳模型是Wnn，RKF，TS的混合。混合模型中使用的技术的顺序非常重要。在此阶段测试了不同订单的不同组合。用不同的技术顺序测试不同的型号。通过使用拟议模型使用不同的看不见的数据集并证明其有效性，验证了拟议的工作。所有提出的模型都是准确的，但最佳模型是Wnn，Ts和RKF的混合。

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