Short-term Wind Power Prediction Based on Improved Firefly Algorithm

Authors

Jiale Ding, School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China;
Guochu Chen, School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China;
Kuo Yuan, School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China;

Abstract

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.

Recommended Citation

Ding, Jiale; Chen, Guochu; and Yuan, Kuo (2019) "Short-term Wind Power Prediction Based on Improved Firefly Algorithm," Journal of System Simulation: Vol. 31: Iss. 11, Article 38.
DOI: 10.16182/j.issn1004731x.joss.19-FZ0322
Available at: https://dc-china-simulation.researchcommons.org/journal/vol31/iss11/38

First Page

2509

Revised Date

2019-07-15

DOI Link

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

Last Page

2516

CLC

TM614

Recommended Citation

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.

DOI

10.16182/j.issn1004731x.joss.19-FZ0322