The Open Electrical & Electronic Engineering Journal

2017, 11 : 48-56
Published online 2017 February 28. DOI: 10.2174/1874129001711010048
Publisher ID: TOEEJ-11-48

RESEARCH ARTICLE
 Short-term Wind Power Prediction Using GA-ELM

Xinyou Wang1, * , Chenhua Wang2 and Qing Li3

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

ABSTRACT

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.