A novel method based on lower–upper bound approximation to predict the wind energy

Abstract

Recently, the prediction of wind power generation has served as a prerequisite problem for determining the objective of the reliability and low–cost power system performance. The difference between conventional deterministic prediction and probabilistic prediction is that the first types ignore the uncertainty of the wind speeds. In this paper, a novel probabilistic prediction method as improved lower–upper bound approximation is applied for building the reliable prediction intervals, which is based on the system that uses the mathematical models of the environment to prognosticate the weather based on existing meteorology (Numeral Weather Prediction System). To verify the worth of the proposed method, forecasting modes in this study are divided as hourly and daily predictions, which are applied to seven wind farms of Taiwan. In addition, adapting parameters in improved lower–upper bound approximation method is performed by resorting to the charged search system algorithm. Also, to increase the prediction speed, the probability density function of wind speed is used, and to decrease the estimation model volume, the kernel density estimation model is applied into the proposed method. The simulation results in comparison with other methods indicate that the prediction intervals produced by the proposed method outperformed and its indices are better. The average error between prediction and real data in the proposed method obtained as about 11%, while other methods error is more than 15%. In addition, three prediction interval indices are used in order to evaluate the different studied manners as: prediction interval overlay probability, normalized average of prediction interval length, and overlay width scale. As the results shown, the best performance is related to the proposed method.