基于改进萤火虫算法的短期风功率预测

doi:10.16182/j.issn1004731x.joss.19-FZ0322

系统仿真学报 ›› 2019, Vol. 31 ›› Issue (11): 2509-2516.doi: 10.16182/j.issn1004731x.joss.19-FZ0322

基于改进萤火虫算法的短期风功率预测

丁家乐, 陈国初, 原阔

上海电机学院电气学院,上海 201306

收稿日期:2019-06-20 修回日期:2019-07-15 出版日期:2019-11-10 发布日期:2019-12-13
作者简介:丁家乐(1996-),女,江苏,硕士生,研究方向为风电功率预测等;陈国初（1971-）,男,江西,博士,教授,研究方向为风力发电设备检测技术、复杂系统建模与仿真。
基金资助:
上海市科委科研项目(17DZ1201200)

Short-term Wind Power Prediction Based on Improved Firefly Algorithm

Ding Jiale, Chen Guochu, Yuan Kuo

School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China

Received:2019-06-20 Revised:2019-07-15 Online:2019-11-10 Published:2019-12-13

摘要/Abstract

摘要： 针对传统的回声状态网络序列对随机性大、波动性强的风功率预测精度不高的问题,提出一种基于改进互补集合经验模态分解和改进萤火虫算法优化回声状态网络的风功率预测模型。利用改进互补集合经验模态分解将风功率数据分解成一系列的本征模态函数,用回声状态网络模型分别对分解后的本征模态函数进行预测,利用改进萤火虫算法寻找模型的最优权值,将输出结果加权合并为最终的风功率预测值。仿真结果表明,所提出的预测方法有较高的预测精度。

关键词: 互补集合经验模态分解, 萤火虫算法, 回声状态网络, 风功率预测

Abstract: A wind power prediction model based on improved complementary set empirical mode decomposition and improved firefly algorithm to optimize echo state network is proposed to solve the problems of low accuracy in wind power with large randomness and strong volatility prediction of traditional echo state network prediction model. The wind speed series are decomposed into a series of intrinsic modes by using the improved complementary ensemble empirical mode decomposition. The new modes are used to predict wind power by using echo state network, the optimal weight of the model is found by the improved firefly algorithm. The output results are weighted and combined into the final wind power prediction value. The simulation results show that the proposed method has higher prediction accuracy.

Key words: complementary ensemble empirical mode decomposition, firefly algorithm, echo state network, wind power prediction

中图分类号:

TM614

丁家乐, 陈国初, 原阔. 基于改进萤火虫算法的短期风功率预测[J]. 系统仿真学报, 2019, 31(11): 2509-2516.

Ding Jiale, Chen Guochu, Yuan Kuo. Short-term Wind Power Prediction Based on Improved Firefly Algorithm[J]. Journal of System Simulation, 2019, 31(11): 2509-2516.

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基于改进萤火虫算法的短期风功率预测

Short-term Wind Power Prediction Based on Improved Firefly Algorithm

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