Ultra-short term wind power prediction based on an error correction stacking method

Abstract

With the increase of the share of wind power in energy distribution, accurate ultra-short term wind power prediction results play key role in the optimal real-time scheduling of the power grid. A stacking integration method is proposed based on error correction in this paper. First, the support vector machine for regression (SVR), gradient boosting decision tree (GBDT), multilayer perceptron (MLP) and random forest (RF) are selected as the base models. Then, the linear regression is utilized as the meta-model. The error generated by the base model in the verification set and the spliced verification set are introduced into the training set of the meta-model. Finally, the prediction results and prediction errors in the prediction set are applied to the meta-model to predict the ultra-short term wind power. The experiment results show that the effectiveness of the proposed method by using the real wind power data.