Short-term Wind Power Prediction Based on Improved Firefly Algorithm

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

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.

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

CLC Number:

TM614

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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