Ultra-Short-Term Multistep Prediction of Wind Power Based on Representative Unit Method

Abstract

With the continuous expansion of wind power grid scale, wind power prediction is an important means to reduce the adverse impact of large-scale grid integration on power grid: the higher prediction accuracy, the better safety, and economy of grid operation. The existing research shows that the quality of input sample data directly affects the accuracy of wind power prediction. By the analysis of measured power data in wind farms, this paper proposes an ultra-short-term multistep prediction model of wind power based on representative unit method, which can fully excavate data information and select reasonable data samples. It uses the similarity measure of time series in data mining, spectral clustering, and correlation coefficient to select the representative units. The least squares support vector machine (LSSVM) model is used as a prediction model for outputs of the representative units. The power of the whole wind farm is obtained by statistical upscaling method. And the number of representative units has a certain impact on prediction accuracy. The case study shows that this method can effectively improve the prediction accuracy, and it can be used as pretreatment method of data. It has a wide range of adaptability.