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

Qunli Wu and Chenyang Peng
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Qunli Wu: Department of Economics and Management, North China Electric Power University, Baoding 071003, China
Chenyang Peng: Department of Economics and Management, North China Electric Power University, Baoding 071003, China

Energies, 2015, vol. 8, issue 12, 1-15

Abstract: 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.

Keywords: wind power grid connected capacity prediction; bat algorithm (BA); least squares support vector machine (LSSVM); Granger causality test (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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