Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine

Xiaohui Yuan, Qingxiong Tan, Xiaohui Lei, Yanbin Yuan and Xiaotao Wu

Energy, 2017, vol. 129, issue C, 122-137

Abstract: Precise prediction of wind power can not only conduct wind turbine's operation, but also reduce the impact on power systems when wind energy is injected into the grid. A hybrid autoregressive fractionally integrated moving average and least square support vector machine model is proposed to forecast short-term wind power. The proposed hybrid model takes advantage of the respective superiority of autoregressive fractionally integrated moving average and least square support vector machine. First, the autocorrelation function analysis is used to detect the long memory characteristics of wind power series, and the autoregressive fractionally integrated moving average model is applied to forecast linear component of wind power series. Then the least square support vector machine model is established to forecast nonlinear component of wind power series by making use of wind speed, wind direction and residual error series of the autoregressive fractionally integrated moving average model. Finally, the prediction of wind power is obtained by integrating the prediction results of autoregressive fractionally integrated moving average and least square support vector machine. Compared with other models, the results of two examples demonstrate that the proposed hybrid model has higher accuracy of wind power prediction in terms of three performance indicators.

Keywords: Wind power prediction; Autoregressive fractionally integrated moving average; Least square support vector machine; Autocorrelation function; Long memory characteristics (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (53)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544217306606
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:129:y:2017:i:c:p:122-137

DOI: 10.1016/j.energy.2017.04.094

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().