基于VMD-BOA-LSSVM-AdaBoost的短期风电功率预测

史彭珍; 魏霞; 张春梅; 谢丽蓉; 叶家豪; 杨家梁

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

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (1) : 226-233. DOI: 10.19912/j.0254-0096.tynxb.2022-1485

基于VMD-BOA-LSSVM-AdaBoost的短期风电功率预测

史彭珍¹, 魏霞¹, 张春梅², 谢丽蓉¹, 叶家豪¹, 杨家梁¹

作者信息 +

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

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

Author information +

文章历史 +

摘要

针对风电信号具有间歇性、非线性、波动性、非平稳性和不确定性等特征,建立一种基于变分模态分解（VMD）和蝴蝶优化算法(BOA)优化最小二乘支持向量机（LSSVM）的风电功率短期预测模型,为提高预测精度,引入自适应校正算法（AdaBoost）。首先,利用变分模态分解将原始功率信号数据分解多个子序列。其次,利用蝴蝶优化算法优化最小二乘支持向量机组合预测模型对每个子序列进行预测。最后通过自适应校正算法将多个分量预测值重构得到最终的预测值,结合西北某一风电场提供的风电功率数据为例验证模型的有效性。结果验证了建立的组合预测模型能够较好地对短期风电功率进行预测,并具有较好的预测精度。

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.

导出引用

史彭珍, 魏霞, 张春梅, 谢丽蓉, 叶家豪, 杨家梁. 基于VMD-BOA-LSSVM-AdaBoost的短期风电功率预测[J]. 太阳能学报. 2024, 45(1): 226-233 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1485

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

中图分类号： TM614 TP181

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