Wind Power Forecasting Based on ARIMA-LGARCH Model

Abstract

Wind power prediction is an important basis for secure and economic operation of power grid. In view of the impacts of wind power’s stochastic volatility on wind power forecasting accuracy, a novel short-term wind power prediction method based on ARIMA-LGARCH model is proposed in this paper. This method analyzes the non-stationary and autocorrelation of time series data of wind power by utilizing Auto Regression Integrated Moving Average (ARIMA) model and asymmetric positive and negative fluctuation characteristics of wind power based on Logarithmic Generalized Autoregression Conditional Heteroscedasticity (LGARCH) model. LGARCH is designed to deal with the asymmetric fluctuation and data-trailing problems of wind power through introducing logarithm process and the independent identically distributed stochastic variable into the current GARCH model. The mixed model of ARIMA-LGARCH is built to achieve high-accuracy short-term forecasting of wind power output with strong uncertainties. The comparative analysis between the predicted value and real value of the actual wind power output in a certain wind farm verifies the feasibility and effectiveness of the method proposed in this paper.