Wind power prediction interval estimation method using wavelet-transform neuro-fuzzy network

Abstract

Wind power point forecasting is the primary method to deal with its uncertainty. However, in many applications, the probabilistic interval of wind power is more useful than traditional point forecasting. Methods to determine the probabilistic interval of wind power point forecasting value is very essential to power system operations. Based on the bootstrap method, this paper proposed a wavelet transform combined with a neuro-fuzzy network model to estimate the prediction interval of wind power. In the model, to account for the ramp event of wind power series, a wavelet-based ramp event was used and the moving block bootstrap method, which considers the dependence of wind power series, was used to construct sampling datasets. Then, the bootstrapped datasets were estimated by a neuro-fuzzy network inference system. A case study provided a 90% confidence level of prediction intervals, which was constructed to examine the effectiveness of the model.