An intelligent hybrid model of neuro Wavelet, time series and Recurrent Kalman Filter for wind speed forecasting

Abstract

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.