GM(2,1) Model and Identification Algorithm Based Wind Power Generation Short-term Prediction

Authors

Ziyun Wang, Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University College of Internet of Things, Jiangsu Province, Wuxi 214122, China;
Zhicheng Ji, Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University College of Internet of Things, Jiangsu Province, Wuxi 214122, China;

Abstract

Abstract: A method based on the gray theory and identification model was proposed to predict the short-term wind power generation. GM(2,1) model was applied for establishing a wind speed prediction model with an iterative step. After the wind speed prediction procedure, a finite impulse response moving average nonlinear Hammerstein model was used in the modeling between wind speed and wind power generation. By adopting the stochastic gradient searching theory, a wind power generation forecasting algorithm was proposed. The proposed simulation shows that the presented method can forecast the real time power generation of wind turbine and raise the accuracy of the wind power prediction, and the simulation that uses the actual data from real wind farm improves the practical applicability of proposed Grey-Identification model.

Recommended Citation

Wang, Ziyun and Ji, Zhicheng (2020) "GM(2,1) Model and Identification Algorithm Based Wind Power Generation Short-term Prediction," Journal of System Simulation: Vol. 27: Iss. 11, Article 20.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss11/20

First Page

2762

Revised Date

2015-05-11

DOI Link

https://doi.org/

Last Page

2769

CLC

TP391.9

Recommended Citation

Wang Ziyun, Ji Zhicheng. GM(2,1) Model and Identification Algorithm Based Wind Power Generation Short-term Prediction[J]. Journal of System Simulation, 2015, 27(11): 2762-2769.