Adaptive forecasting of wind power based on selective ensemble of offline global and online local learning

Wind power has become an important part of clean energy. Reliable wind power forecasting is the key to performing optimal scheduling of wind energy. However, it is challenging to obtain accurate wind power forecasting due to inherent intermittency and randomness of wind energy. In addition, the characteristics of wind power data will inevitably change with time, which will lead to the performance degradation of the forecasting model. To address these problems, an adaptive wind power forecasting method based on selective ensemble of offline global and online local learning (SEOGOL) is proposed. To ensure the effectiveness of the ensemble, SEOGOL employs a multi-modal perturbation mechanism to enhance the diversity of the base models, including perturbation on modeling samples through multi-resolution and multi-interval time-difference resampling, perturbation on learners, and perturbation on model hyperparameters. In addition, with defining the accuracy and diversity of base models as two optimization objectives, a multi-objective ensemble pruning approach is proposed to achieve model selection. Furthermore, the adaptive fusion of the individual models is achieved according to the Bayesian rule. Compared with traditional non-adaptive and simple adaptive forecasting methods, the proposed method successfully combines the advantages of offline global and online local learning. SEOGOL can not only mine large-scale historical wind power data, but also capture the latest wind power status information, thus effectively improving the accuracy and reliability of wind power forecasting. The effectiveness and superiority of the proposed SEOGOL method are verified using an actual wind power data set.