Advances in New and Renewable Energy >
Wind Power Forecasting Base on ARMAX-GARCH for a Microgrid
Received date: 2013-07-29
Revised date: 2013-11-27
Online published: 2013-12-27
Wind power forecasting models are often built for point forecasting using wind power series with large sampling intervals, which reduces the accuracy and reliability of the forecasting models. Based on wind power series with small sampling interval, this paper proposes an ARMAX-GARCH wind power forecasting model. The ARMAX model is built for point forecasting by combining the constructed innovation series of wind power and the hourly-average wind power series. The model identification and exogenous covariates selection are based on Bayesian information criterion (BIC) and correlation analysis. The GARCH model is used for interval forecasting to simulate the fluctuation characteristic of the residual series. To demonstrate the effectiveness, the model for 1 h ahead forecasting is applied and tested on a microgrid located on an island in the south of China. Comparing with persistent method, ARMA and RBF neural network, simulation results demonstrate that the proposed forecasting model improves the accuracy of point forecasting significantly and has a better interval forecasting result.
HUANG Lei , SHU Jie , CUI Qiong , JIANG Gui-xiu . Wind Power Forecasting Base on ARMAX-GARCH for a Microgrid[J]. Advances in New and Renewable Energy, 2013 , 1(3) : 224 -229 . DOI: 10.3969/j.issn.2095-560X.2013.03.004
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