Open Access

ARTICLE

Research on the IL-Bagging-DHKELM Short-Term Wind Power Prediction Algorithm Based on Error AP Clustering Analysis

Jing Gao^*, Mingxuan Ji, Hongjiang Wang, Zhongxiao Du

School of Electric Power, Shenyang Institute of Engineering, Shenyang, 100083, China

* Corresponding Author: Jing Gao. Email:

Computers, Materials & Continua 2024, 79(3), 5017-5030. https://doi.org/10.32604/cmc.2024.050158

Received 29 January 2024; Accepted 06 May 2024; Issue published 20 June 2024

Abstract

With the continuous advancement of China’s “peak carbon dioxide emissions and Carbon Neutrality” process, the proportion of wind power is increasing. In the current research, aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data, a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine (IL-Bagging-DHKELM) error affinity propagation cluster analysis is proposed. The algorithm effectively combines deep hybrid kernel extreme learning machine (DHKELM) with incremental learning (IL). Firstly, an initial wind power prediction model is trained using the Bagging-DHKELM model. Secondly, Euclidean morphological distance affinity propagation AP clustering algorithm is used to cluster and analyze the prediction error of wind power obtained from the initial training model. Finally, the correlation between wind power prediction errors and Numerical Weather Prediction (NWP) data is introduced as incremental updates to the initial wind power prediction model. During the incremental learning process, multiple error performance indicators are used to measure the overall model performance, thereby enabling incremental updates of wind power models. Practical examples show the method proposed in this article reduces the root mean square error of the initial model by 1.9 percentage points, indicating that this method can be better adapted to the current scenario of the continuous increase in wind power penetration rate. The accuracy and precision of wind power generation prediction are effectively improved through the method.

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