AN ULTRA-SHORT-TERM WIND POWER PREDICTION METHOD BASED ON MGWO-INFORMER

Wang Jie; Liu Xingjie; Liang Ying; Bo Tianli

doi:10.19912/j.0254-0096.tynxb.2023-1192

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (11) : 477-485. DOI: 10.19912/j.0254-0096.tynxb.2023-1192

AN ULTRA-SHORT-TERM WIND POWER PREDICTION METHOD BASED ON MGWO-INFORMER

Wang Jie, Liu Xingjie, Liang Ying, Bo Tianli

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Abstract

Aiming at the problems of inaccurate hyper-parameter optimization and low prediction accuracy of Informer in wind power prediction, an ultra-short-term wind power prediction model based on MGWO-Informer is proposed. Firstly, in order to solve the shortcomings of the traditional Grey Wolf evolutionary algorithm, which has the disadvantages of poor solution accuracy and easy to fall into the local optimum in some of the optimization searching processes, a Modified Grey Wolf Optimizer （MGWO） algorithm is proposed to enhance the global searching, which proposes 2 kinds of improvement strategies： the new convergence factor and the staged position updating. Second, the Informer model parameters are optimized using the improved Gray Wolf algorithm to improve the accuracy of model hyperparameter optimization. Finally, taking the measured data of a wind farm in Northwest China as an example, the prediction results are compared with gate recurrent unit （GRU）, long short term memory （LSTM） and transformer. The results indicate that the improved Informer model has high prediction accuracy and operational efficiency, with a decrease of 26.74% in mean absolute error and 19.74% in root mean square error, and an increase of 1.41% in fitting coefficient.

Key words

wind power / evolutionary algorithms / prediction models / Informer model / ultra-short-term

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Wang Jie, Liu Xingjie, Liang Ying, Bo Tianli. AN ULTRA-SHORT-TERM WIND POWER PREDICTION METHOD BASED ON MGWO-INFORMER[J]. Acta Energiae Solaris Sinica. 2024, 45(11): 477-485 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1192

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