Short-term Wind Power Prediction Using GA-ELM

Xinyou Wang1, *, Chenhua Wang2, Qing Li3
1 Institute of Technology, Gansu Radio & TV University, Lanzhou 730030, P.R. China
2 Northwest Engineering Corporation Limited, PowerChina, Xi'an 710065, P.R. China
3 State Grid Xinjiang Electric Power Company, Electric Power Research Institute, Grid technology Center, Urumqi 830000, P.R. China

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© 2017 Wang et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Institute of Technology, Gansu Radio & TV University, Lanzhou, P.R. China; Tel: 18293131209; E-mail:


Focusing on short-term wind power forecast, a method based on the combination of Genetic Algorithm (GA) and Extreme Learning Machine (ELM) has been proposed. Firstly, the GA was used to prepossess the data and effectively extract the input of model in feature space. Basis on this, the ELM was used to establish the forecast model for short-term wind power. Then, the GA was used to optimize the activation function of hidden layer nodes, the offset, the input weights, and the regularization coefficient of extreme learning, thus obtaining the GA-ELM algorithm. Finally, the GA-ELM was applied to the short-term wind power forecast for a certain area. Compared with single ELM, Elman algorithms, the experimental results show that the GA-ELM algorithm has higher prediction accuracy and better ability for generalization.

Keywords: Short-term prediction, Wind power prediction, Genetic algorithm, Extreme learning machine, GA-ELM, NWP.