Corrected multi-resolution ensemble model for wind power forecasting with real-time decomposition and Bivariate Kernel density estimation

Abstract

The power integration is a challenge for the power system because of the fluctuation of the wind power. Wind power forecasting can estimate the future fluctuation of the wind power, and enhance the safety of the power integration. In this study, a corrected multi-resolution forecasting model is proposed to improve current wind power forecasting performance. The proposed model contains three stages, including multi-resolution ensemble, adaptive multiple error corrections and uncertainty estimation. Four real-time wind power data sets are applied to verify the effectiveness of the proposed model. The results are shown as follows: (a) the proposed model is effective for wind power forecasting, the 1-step index of agreement and coverage width-based criterion with 99% confidence level of the proposed model on the dataset #1 are 0.9432 and 0.6951 respectively; (b) the proposed model outperforms the previous models. Through techno-economic analysis, it can be concluded that the proposed model has the potential to be applied to improve the power integration performance.