A hybrid model with combined feature selection based on optimized VMD and improved multi-objective coati optimization algorithm for short-term wind power prediction

被引：0

作者：

Wang, Chao ^{[1
,2
]}

Lin, Hong ^{[1
]}

Hu, Heng ^{[1
,3
]}

Yang, Ming ^{[2
,4
]}

Ma, Li ^{[2
]}

机构：

[1] School of Electrical Engineering, Xinjiang University, Xinjiang, Urumqi,830047, China

[2] School of Energy and Control Engineering, Changji University, Changji,831100, China

[3] School of Energy Engineering, Xinjiang Institute of Engineering, Urumqi,830023, China

[4] Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Jinan,250061, China

来源：

Energy | 2024年 / 293卷

基金：

中国国家自然科学基金;

关键词：

Feature Selection - Multiobjective optimization - Weather forecasting - Wind power;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

With the continuous global increase in installed wind power capacity and subsequent surge in power generation, the contradiction between the safe operation of the grid and the efficient consumption of new energy after large-scale grid connection has become increasingly prominent. This paper presents a hybrid model prediction method to further improve the stability and accuracy of wind power prediction. Firstly, variational modal decomposition (VMD) optimized by the coati optimization algorithm (COA) is employed to decompose original wind power, mitigating the non-stationary characteristics of the power sequence. Subsequently, permutation entropy (PE) is used to recombine the decomposed components, and the combined feature selection method is achieved by integrating the Spearman correlation coefficient (SCC) and the autocorrelation function (ACF). Then, the multivariate combined model is constructed, and the improved multi-objective coati optimization algorithm (IMOCOA) determines the weight coefficients of each model to enhance the performance of the hybrid model. Finally, research and analysis are conducted from multiple scenarios and time scales using historical operating data from a wind farm in Xinjiang. The experimental results show that the proposed prediction model effectively improves the accuracy and stability of the wind power prediction compared with other popular prediction models. © 2024 Elsevier Ltd

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