A new decomposition-ensemble strategy fusion with correntropy optimization learning algorithms for short-term wind speed prediction

Reliable short-term wind speed prediction is critical for ensuring the rational exploitation and utilization of wind energy. However, due to complex characteristics (e.g., nonstationarity, nonlinearity, uncertainty, etc.) of natural winds, the realization of this task usually confronts a great challenge. For this purpose, an innovative method for forecasting short-term wind speeds is developed based on the principle of “decomposition-prediction-ensemble”. Concretely, a new pretreatment technique, including boxplot figure based abnormal data diagnosis and correction, multivariate fast iterative filtering based time-frequency decomposition, and improved amplitude and frequency modulation based subseries reconstruction, is first developed to perform the high-quality data preprocessing. Then, three different algorithms in conjunction with the correntropy loss and consideration of model diversity are designed as high-performance predictors to capture more data characteristics. Further, a hybrid ensemble strategy combining stacking ensemble and hierarchical ensemble is developed to learn the potential interaction or nonlinear correlation among decomposed subseries as well as some uncertain information for high-reliability prediction. The eventual predictions are given in the form of deterministic point-value, interval, and real-time probability density function. Numerical examples based on four sets of multi-height wind speed data prove the effectiveness and superiority of the proposed method. For example, the average promotion obtained by this method compared with univariate conditional kernel density estimation in terms of mean absolute error is 33.87%, while the improvement in terms of coverage width criterion is 29.64%.