A novel hybrid machine learning model for short-term wind speed prediction in inner Mongolia, China.

Wind power is recognized as one of the most promising renewable and clean energy sources under the context of the increasing depletion of fossil fuels. The exact wind speed forecasting has great significance for the large-scale connection of wind farms with the power grid. In light of this, this paper contributes to establishing a novel hybrid model that can predict the future wind speed accurately. Firstly, the original wind speed time series is decomposed by the fast ensemble empirical mode decomposition into several sub-series that are further integrated by the runs test. The phase space reconstruction is used to dynamically choose each integrated sub-series' input and output vectors for the prediction model. Additionally, an improved whale optimization algorithm is exploited to optimize the weights and bias of the extreme learning machine. Finally, prediction results are obtained from the aggregation of each integrated sub-series prediction. To verify the accuracy and applicability of the proposed hybrid model, we apply several comparative models to conducted two case studies using different wind speed time series from Inner Mongolia that is Asia's largest gathering area of wind power farms. According to the experimental results, it can be concluded that the decomposition reduces the volatility and randomness of wind speed, and the runs test lowers the forecasting complexity. The phase space reconstruction can capture the chaotic property of wind speed series. The optimization for the whale optimization algorithm enhances its global and local optimization ability to further improve the performance of extreme learning machine. Overall, the proposed hybrid model can effectively capture the non-linear characteristics of wind speed series. • A novel hybrid short-term wind speed prediction model is proposed. •The model of FEEMD-RT-PSR is used for the data preprocessing. •Whale optimization algorithm is optimized. •The performance of extreme learning machine is improved. [ABSTRACT FROM AUTHOR]