Abstract
Interval prediction of wind power, which features the upper and lower limits of wind power at a given confidence level, plays a significant role in accurate prediction and stability of the power grid integrated with wind power. However, the conventional methods of interval prediction are commonly based on a hypothetic probability distribution function, which neglects the correlations among various variables, leading to decreased prediction accuracy. Therefore, in this paper, we improve the multi-objective interval prediction based on the conditional copula function, through which we can fully utilize the correlations among variables to improve prediction accuracy without an assumed probability distribution function. We use the multi-objective optimization method of non-dominated sorting genetic algorithm-II (NSGA-II) to obtain the optimal solution set. The particular best solution is weighted by the prediction interval average width (PIAW) and prediction interval coverage probability (PICP) to pick the optimized solution in practical examples. Finally, we apply the proposed method to three wind power plants in different Chinese cities as examples for validation and obtain higher prediction accuracy compared with other methods, i.e., relevance vector machine (RVM), artificial neural network (ANN), and particle swarm optimization kernel extreme learning machine (PSO-KELM). These results demonstrate the superiority and practicability of this method in interval prediction of wind power.
Highlights
China is rich in wind resources [1, 2] and has adopted a national policy to vigorously develop wind power
The interval prediction results can be obtained via establishment of the probability distribution function of wind power prediction based on the empirical distribution model and a non-parametric regression technique [22]
This paper improved the wind power prediction method based on the conditional copula function and proposed a combination of this method with multi-objective optimization algorithms, supplying reference information for shortterm or real-time dispatch of the power system
Summary
China is rich in wind resources [1, 2] and has adopted a national policy to vigorously develop wind power. The non-parametric kernel density estimation method fits the probability distribution curve of wind power through interpolation and obtains the prediction interval that satisfies the preset confidence level [20]. Based on the standardized Gaussian distribution, Kou Peng et al used a modified Gaussian model to realize interval prediction of wind power [21]. The interval prediction results can be obtained via establishment of the probability distribution function of wind power prediction based on the empirical distribution model and a non-parametric regression technique [22]. To improve the interval coverage and reduce the interval width, Fan Lei et al [23] used the method of ensemble empirical mode decomposition and correlation vector machine to realize short-term wind power interval prediction
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