A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: A case study of wind farms in northwest China

Abstract

Forecasting wind speed is a crucial issue in the wind power industry, and wind energy is gradually coming to be viewed as one of the most promising alternative energy sources because of its cleanliness and renewability for large-scale commercial production. With the aim of developing accurate tools for forecasting wind speed, this paper presents a novel hybrid intelligent forecasting model based on Least Square Support Vector Machine and the Markov model. Prior to conducting the wind speed forecasting, the original wind speed series is processed using the C–C method, which is a phase space reconstruction method, to automatically determine the input form. Subsequently, the LSSVM (least squares support vector machine) model, which is optimized using the PSOGSA (partical swarm optimization combined with gravitational search algorithm) algorithm, is employed to forecast wind speed. Additionally, the Markov model is developed to make error corrections with the state ranges determined using the FCM (fuzzy C-means) model. The proposed model takes the average forecasting values between the outputs generated by LSSVM and error corrected LSSVM model as the final forecasting results. The simulation results indicate that the proposed model can outperform the other models discussed in this paper with respect to forecasting accuracy and can be a suitable tool for forecasting wind speeds.