Simulation of Wind Power Prediction Based on Improved ELM

Authors

Wang Hao, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Wuxi 214122, China;
Wang Yan, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Wuxi 214122, China;
Zhicheng Ji, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Wuxi 214122, China;

Abstract

Abstract: To predict the range of ultra-short-term wind power fluctuation effectively, a combined forecasting model based on fuzzy information granulation (FIG) and genetic algorithm optimization extreme learning machine (GA-ELM) is proposed. The parameters of wind power are granulated by fuzzy information, and the corresponding valid information including the maximal value, the minimum value, and the general average value in time series window is further extracted. By integrating the effective components of each parameter as training samples, the GA-ELM-based prediction model is established. The range of wind power fluctuation in next time series is forecasted through using the optimized model. The experimental results demonstrate that the combined prediction model can effectively track some variations in wind power and predict the range of wind power fluctuation.

Recommended Citation

Hao, Wang; Yan, Wang; and Ji, Zhicheng (2019) "Simulation of Wind Power Prediction Based on Improved ELM," Journal of System Simulation: Vol. 30: Iss. 11, Article 46.
DOI: 10.16182/j.issn1004731x.joss.201811046
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss11/46

First Page

4437

Revised Date

2018-06-02

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201811046

Last Page

4447

CLC

TP391.9

Recommended Citation

Wang Hao, Wang Yan, Ji Zhicheng. Simulation of Wind Power Prediction Based on Improved ELM[J]. Journal of System Simulation, 2018, 30(11): 4437-4447.

DOI

10.16182/j.issn1004731x.joss.201811046