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

Ziqi Zhang; Yunfei Ding; Jin Yang

doi:10.1117/12.2689397

10 August 2023 Ultra-short term wind power prediction based on an error correction stacking method

Ziqi Zhang, Yunfei Ding, Jin Yang

Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 127483J (2023) https://doi.org/10.1117/12.2689397
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China

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.

Citation Download Citation

Ziqi Zhang, Yunfei Ding, and Jin Yang "Ultra-short term wind power prediction based on an error correction stacking method", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 127483J (10 August 2023); https://doi.org/10.1117/12.2689397

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