SHORT TERM WIND POWER INTERVAL PREDICTION BASED ON EVMD AND CUCKOO ALGORITHM

Zhang Yagang; Zhao Yunpeng; Wang Siqi

doi:10.19912/j.0254-0096.tynxb.2020-1333

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (8) : 292-299. DOI: 10.19912/j.0254-0096.tynxb.2020-1333

SHORT TERM WIND POWER INTERVAL PREDICTION BASED ON EVMD AND CUCKOO ALGORITHM

Zhang Yagang, Zhao Yunpeng, Wang Siqi

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Abstract

In order to improve the accuracy and controllability of short-term wind power prediction, a short-term wind power prediction model based on energy difference optimal variational mode decomposition and cuckoo optimal combination neural network is proposed. The energy difference is used to optimize the number of modes of the variational modal decomposition （EVMD）, and EVMD is used for short-term wind power decomposition. Based on the different modal characteristics of the EVMD decomposition sequence, a cuckoo optimized back propagation neural network is used for the nonlinear sequence （CS-BPNN）. The autoregressive moving average model （ARMA） is used for the stationary series, and the weighted point prediction value is reconstructed. The kernel density estimation is constructed based on the sequence information lost by EVMD decomposition. Based on the point prediction model, the interval prediction of wind power is carried out. The proposed prediction method is applied to a practical example of wind farms in Australia. The experimental results show that the method can improve the accuracy of short-term wind power prediction.

Key words

wind power / forecasting / signal processing / EVMD / neural network / cuckoo algorithm

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Zhang Yagang, Zhao Yunpeng, Wang Siqi. SHORT TERM WIND POWER INTERVAL PREDICTION BASED ON EVMD AND CUCKOO ALGORITHM[J]. Acta Energiae Solaris Sinica. 2022, 43(8): 292-299 https://doi.org/10.19912/j.0254-0096.tynxb.2020-1333

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