Short-term wind power prediction based on wavelet decomposition and extreme learning machine

Authors:
Xin Wang;Yihui Zheng;Lixue Li;Lidan Zhou;Gang Yao;Ting Huang
Affiliations:
Center of Electrical & Electronic Technology, Shanghai Jiao Tong University, Shanghai, P.R.China,School of Electrical and Electronic Engineering, East China Jiaotong University, Nanchang, Jian ...;Center of Electrical & Electronic Technology, Shanghai Jiao Tong University, Shanghai, P.R.China;Center of Electrical & Electronic Technology, Shanghai Jiao Tong University, Shanghai, P.R.China;Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai, P.R.China;Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai, P.R.China;Center of Electrical & Electronic Technology, Shanghai Jiao Tong University, Shanghai, P.R.China
Venue:
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Year:
2012

Citing 2
Cited 0

Composite Function Wavelet Neural Networks with Differential Evolution and Extreme Learning Machine

Neural Processing Letters
Ultra-short term prediction of wind power based on multiples model extreme leaning machine

ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

Wind energy has been widely used as a renewable green energy all over the world. Due to the stochastic character in wind, the uncertainty in wind generation is so large that power grid with safe operation is challenge. So it is very significant to design an algorithm to forecast wind power for grid operator to rapidly adjust management planning. In this paper, based on the strong randomness of wind and the short precision of BP network forecasting, Short-Term Power Prediction of a Wind Farm Based on Wavelet Decomposition and Extreme Learning Machine (WD-ELM) is proposed. Signal was decomposed into several sequences in different band by wavelet decomposition. Decomposed time series were analyzed separately, then building the model for decomposed time series with ELM to predict. Then the predicted results were added. Through a wind-power simulation analysis of a wind farm in Inner Mongolia, the result shows that the method in this paper has higher power prediction precision compared with other methods.