Simulation of Wind Power Prediction Based on Improved ELM

doi:10.16182/j.issn1004731x.joss.201811046

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.

Key words: wind power, combined prediction, fuzzy information granulation, genetic algorithm, extreme learning machine

CLC Number:

TP391.9

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.

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