Enhanced Wind Generation Forecast Using Robust Ensemble Learning

This letter proposes a robust ensemble learning scheme to enhance short-term prediction of wind power generation. The ensemble problem associated with pruning and combination is formulated as a worst-case robust approximation problem, taking forecast uncertainty in individual predictors into consideration. This problem is then transformed into the scaled form of the augmented Lagrangian and is solved via the alternating direction method of multipliers (ADMM). The proposed scheme can be applied to both deterministic and probabilistic forecasting. A comprehensive study is carried out to illustrate the advantage of the proposed scheme in both point and interval forecasting.

Automated summary

This study proposes a robust ensemble learning scheme to enhance short-term wind power generation prediction, by taking into account forecast uncertainty in individual predictors. Through a comprehensive study on both point and interval forecasting, the advantage of the proposed scheme is illustrated.