摘要
为提高风电功率预测的精度,提出了一种基于互补集合经验模态分解(complementary ensemble empirical mode decomposition,CEEMD)、缎蓝园丁鸟优化算法(satinbower birdoptimizationalgorithm,SBO)及最小二乘支持向量回归(least squares support vector regression,LSSVR)模型的超短期风电功率组合预测方法。针对风电序列的随机波动性,采用CEEMD对风电功率序列进行分解,将分解得到的不同特征尺度的各分量作为LSSVR模型的训练输入量。引入SBO算法对LSSVR的正则化参数与核函数宽度进行优化,建立各分量的预测模型,将各分量的预测输出值叠加得到最终的风电功率预测值。所提CEEMD-SBO-LSSVR组合预测方法不仅有效降低了预测的复杂度,而且保证原始风电序列经模态分解处理后具有小的重构误差。仿真结果表明,与其他预测模型相比,所提方法具有较高的超短期风电功率预测精度。
To improve the accuracy of wind power forecasting,an ultra-short-term wind power combined prediction based on the complementary ensemble empirical mode decomposition(CEEMD)the satin bower bird optimization algorithm(SBO)and the optimized least squares support vector regression(LSSVR)is proposed.With the random volatility of wind power sequences,the CEEMD is used to decompose the wind power data,and the decomposed series of components with different time characteristic scales are used as the training inputs for the LSSVR model.Then SBO is introduced to optimize the regularization parameter and the width of the kernel function of the LSSVR,and the wind power prediction model is established for each component.The final predicted values can be obtained by superimposing the prediction value of each component.The combined prediction based on the CEEMD-SBO-LSSVR not only effectively reduces the complexity of prediction,but also ensures the original wind power sequence’s a small reconstruction error after the modal decomposition.The simulation results illustrate that this method has higher prediction accuracy for ultra-short-term wind power than other predictions.
作者
周小麟
童晓阳
ZHOU Xiaolin;TONG Xiaoyang(School of Electrical Engineering,Southwest Jiaotong University,Chengdu 610031,Sichuan Province,China)
出处
《电网技术》
EI
CSCD
北大核心
2021年第3期855-862,共8页
Power System Technology
关键词
超短期风电预测
最小二乘支持向量回归
互补集合经验模态分解
缎蓝园丁鸟优化算法
组合模型
ultra-short-term wind power prediction
least squares support vector regression(LSSVR)
complementary ensemble empirical mode decomposition(CEEMD)
the satin bower bird optimization algorithm(SBO)
combination model
作者简介
周小麟(1996),男,硕士研究生,主要研究方向为人工智能技术在电力系统中的应用,E-mail:xlzhou@my.swjtu.edu.cn;通讯作者:童晓阳(1970),男,博士,副教授,博士生导师,主要研究方向为电网故障诊断、广域后备保护、智能变电站、能源互联网,E-mail:xytong@swjtu.cn。
