Wind Power Grid Connected Capacity Prediction Using LSSVM Optimized by the Bat Algorithm

Energies

Qunli Wu²  Chenyang Peng¹ 

[1] Department of Economics and Management, North China Electric Power University, Baoding 071003, China

关键词: wind power grid connected capacity prediction;    bat algorithm (BA);    least squares support vector machine (LSSVM);    Granger causality test;   

DOI  :  10.3390/en81212428

来源: mdpi

【 摘 要 】

Given the stochastic nature of wind, wind power grid-connected capacity prediction plays an essential role in coping with the challenge of balancing supply and demand. Accurate forecasting methods make enormous contribution to mapping wind power strategy, power dispatching and sustainable development of wind power industry. This study proposes a bat algorithm (BA)–least squares support vector machine (LSSVM) hybrid model to improve prediction performance. In order to select input of LSSVM effectively, Stationarity, Cointegration and Granger causality tests are conducted to examine the influence of installed capacity with different lags, and partial autocorrelation analysis is employed to investigate the inner relationship of grid-connected capacity. The parameters in LSSVM are optimized by BA to validate the learning ability and generalization of LSSVM. Multiple model sufficiency evaluation methods are utilized. The research results reveal that the accuracy improvement of the present approach can reach about 20% compared to other single or hybrid models.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.