A combined short-term forecast model of wind power based on empirical mode decomposition and augmented dickey-fuller test

Abstract

The high volatility of wind power time series is an important factor that affects its forecasting results. Hence, it is necessary to analyze and preprocess the historical data. To improve the accuracy of wind power forecasting, a two-predictor combined model based on two data processing algorithms, empirical mode decomposition and augmented Dickey-Fuller test, is proposed in this paper. First, the original wind power time series is decomposed into several sub-components by the empirical mode decomposition algorithm. Second, the augmented Dickey-Fuller test is employed to test the stationarity of each sub-component, and the sub-components are divided into two categories: stationary and non-stationary. Third, the stationary components are forecasted by least-square support vector machine while the non-stationary ones are forecasted by the persistence model. Finally, the prediction value is the summary of the results of the sub-components. Three models, least-square support vector machine, the persistence model and the empirical mode decomposition-least-square support vector machine, are used to compare the performance with the proposed model on two real wind power datasets. The analysis results indicate that the proposed model can achieve higher forecast accuracy and stability than other models.