A novel probabilistic short-term wind energy forecasting model based on an improved kernel density estimation

Abstract

The evolution of renewable energy especially wind energy over the past decade has surpassed all expectations. Short-term probabilistic wind power forecasting is a good option to increase the reliability of power system. But short-term forecasting of power generated by wind turbine has high error due to uncertainty parameter such as wind speed so, it is very important to find a way to increase the accuracy of forecasting. Therefore, in this paper, a novel forecasting method that has high reliability compared to other methods is presented. In this paper, in order to benefit the superiority of various prediction models, a new Improved Kernel Density Estimation (IKDE) method is exploited to estimate the wind energy possibility. The combination of various prediction models and the suggested method might develop the function of probabilistic prediction by supplying divergent types of compactness performance. KDE method is a powerful method to analyze background and foreground characteristic. In order to increase the efficiency of KDE method some of predicting model are combined and a detection algorithm based on an Improved Kernel Density Estimation (IKDE) model is defined. The appropriate bandwidths, comparative threshold, comparative background sample learning array, and an enhanced sample updating model for sample learning array are proposed as the basics of the IKDE model. Two levels of optimization are used to simplify the IKDE model parameters. Finally, in order to prove the superiority of the proposed method over other methods, this method and 4 other methods have been implemented on 10 wind farms. The simulation results show that the prediction accuracy of the proposed method is about 3.8% higher than other methods due to the improved structure.