The Open Fuels & Energy Science Journal

2016, 9 : 65-76
Published online 2016 October 20. DOI: 10.2174/1876973X01609010065
Publisher ID: TOEFJ-9-65

RESEARCH ARTICLE
 Hybrid Forecasting Model Based Data Mining and Cuckoo Search: A Case Study of Wind Speed Time Series

Xiangdong Xu1 , Xi Song1, * , Qian Wang1 , Zhiyuan Liu1 , Jing Wang1 and Zhiru Li2

* Address correspondence to this author at the State Grid Gansu Electric Power Company, Lanzhou 730030, ; Tel: +86 0931-2966361; E-mail: songxi_sgcc@163.com

ABSTRACT

Wind energy has been part of the fastest growing renewable energy sources that is clean and pollution-free, which has been increasingly gaining global attention, and wind speed forecasting plays a vital role in the wind energy field, however, it has been proven to be a challenging task owing to the effect of various meteorological factors. This paper proposes a hybrid forecasting model, which can effectively make a preprocess for the original data and improve forecasting accuracy, the developed model applies cuckoo search(CS) algorithm to optimize the parameters of the wavelet neural network (WNN) model. The proposed hybrid method is subsequently examined on the wind farms of eastern China and the forecasting performance shows that the developed model is better than some traditional models.

Keywords:

Cuckoo search, Data mining, Hybrid forecasting, Parameter optimization, Wind speed forecasting, Wavelet neural network.