SHORT-TERM WIND POWER PREDICTION BASED ON VMD-BOA-LSSVM-AdaBoost

Shi Pengzhen; Wei Xia; Zhang Chunmei; Xie Lirong; Ye Jiahao; Yang Jialiang

doi:10.19912/j.0254-0096.tynxb.2022-1485

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (1) : 226-233. DOI: 10.19912/j.0254-0096.tynxb.2022-1485

SHORT-TERM WIND POWER PREDICTION BASED ON VMD-BOA-LSSVM-AdaBoost

Shi Pengzhen¹, Wei Xia¹, Zhang Chunmei², Xie Lirong¹, Ye Jiahao¹, Yang Jialiang¹

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Abstract

Aiming at the intermittent, nonlinear, fluctuating, non-stationary and uncertain characteristics of wind power signals, the short-term forecasting method for wind power is established, which is based on Variational mode decomposition（VMD） and butterfly optimization algorithm（BOA）to optimize least squares support vector machine（LSSVM） and introducing adaptive correction to improve accuracy. Firstly, the raw power signal data is splitted into multiple subsequences by using VMD. Secondly, BOA is used to optimize combined prediction model of LSSVM to predict each subsequence. Finally, the prediction value of multiple components is reconstructed through AdaBoost to obtain the final prediction value. Combined with the wind power data provided by a wind farm in Northwest China as an example, the effectiveness of the model is verified. The results show that the combined forecasting model established above can predict the short-term wind power well and has a good forecasting accuracy.

Key words

wind power prediction / LSSVM / VMD / AdaBoost / prediction accuracy

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Shi Pengzhen, Wei Xia, Zhang Chunmei, Xie Lirong, Ye Jiahao, Yang Jialiang. SHORT-TERM WIND POWER PREDICTION BASED ON VMD-BOA-LSSVM-AdaBoost[J]. Acta Energiae Solaris Sinica. 2024, 45(1): 226-233 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1485

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