Accurate prediction of ultra-short-term wind speed is important in many applications. Because of the different patterns of wind speed, researchers have indicated that a multi-scale prediction method based on wavelet algorithms could improve forecasting. However, traditional multi-scale methods that directly synthetize results of different-scale components may reduce forecasting accuracy because of error accumulation. In this paper, a multi-scale synthesis strategy that takes into account the predictability of different scales is proposed. As the correlation length of each frequency sub-series is different, prediction models are constructed for each frequency sub-series using different numbers of prediction steps. Finally, based on two wind farms in different regions of China and different basic forecast models, eight experiments are performed using real-world wind speed data. Experimental results show that the proposed multi-scale synthesis method has better performance than traditional multi-scale forecasting methods that use direct synthetizing strategies, indicating its utility in wind engineering applications.
['Jie Wan', 'Guorui Ren', 'Jinfu Liu', 'Qinghua Hu', 'Daren Yu']
Ultra-short-term wind speed prediction based on multi-scale predictability analysis