A cooperative ensemble method for multistep wind speed probabilistic forecasting

作者：He, Yaoyao^*; Wang, Yun; Wang, Shuo; Yao, Xin

来源：Chaos, Solitons and Fractals, 2022, 162: 112416.

DOI：10.1016/j.chaos.2022.112416

• Wind speed forecasting
• Cooperative ensemble method
• CEEMDAN decomposition
• Multistep probabilistic forecasting

Accurate wind speed forecasting is of great significance to ensure the safe utilization of wind power. However, the randomness and volatility nature of wind speed give rise to an enormous challenge to the precision of wind speed forecasting. Combining the data preprocess technology, feature selection method, forecasting model, optimization algorithm and data postprocessing technology, the complete ensemble empirical mode de-composition with adaptive noise-least absolute shrinkage and selection operator-quantile regression neural net-work (CEEMDAN-LASSO-QRNN) model is developed to preform multistep wind speed probabilistic forecasting. Within the proposed model, CEEMDAN technology is firstly employed to decompose original wind speed timeseries into several subsequences. For each subsequence, the explanatory variables constructed by a hybrid multistep forecasting strategy are selected by LASSO regression. Subsequently, QRNN forecasting models are es-tablished to obtain multistep conditional quantiles predictions for entire subsequences. Ultimately, the aggre-gated quantiles are served as the samples to fit approximate distribution through kernel density estimation (KDE), thus obtaining the probability density function, further achieving probabilistic predictions, interval pre-dictions and point predictions. The case studies including four real datasets are provided to validate the depend-ability and feasibility of the proposed model. Experimental results indicate higher accuracy and robustness of the proposed model occur in multistep wind speed probabilistic forecasting.