Wind and solar power probability density prediction via fuzzy information granulation and support vector quantile regression

Abstract

Uncertainty among wind and solar power affects the stability of power systems. In order to fully describe the uncertainty of wind and solar power, a probability density prediction model is proposed to predict the probability density function of wind and solar power. According to the wind and solar power time series, the original data is processed with fuzzy information granularity to eliminate the fluctuation and uncertainty of data. The Lagrange function is constructed by a support vector quantile regression model to get the quantile of wind and solar power at different points. The conditional quantiles are combined with the Epanechnikov kernel function to acquire complete probability density curves of forecasting results. In order to evaluate the performance of the output results, this paper analyzes the accuracy of the prediction results using point prediction error, prediction interval coverage probability and average bandwidth. The experimental data of wind and solar power with same temporal and spatial resolutions are taken into account. The results show that the method can effectively describe the uncertainty of wind and solar power, and also provide technical support for the safe and stable operation of the power system.