Multi-objective interval prediction of wind power based on conditional copula function

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 multiobjective 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 nondominated 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.

A Review on Plug-in Electric Vehicles: Introduction, Current Status, and Load Modeling Techniques

“…For this purpose, the copula function can be used that characterizes the dependencies between the variables and creates the unique distribution for correlated multi-variate data modeling. More details about copula method can be found in [108].…”

Combined Dispatching of Hydropower and Wind Power Based on the Hedging Theory

“…The relationship between water level and storage capacity of the reservoir and the relationship between downstream water level and flow is shown in Figure 6. According to the wind power interval prediction model based on the Copula function established in the Ref [38], the interval prediction of the original wind power output is carried out. The confidence level is selected as 0.93.…”

“…The result is shown in Figure 7. According to the wind power interval prediction model based on the Copula function established in the Ref [38], the interval prediction of the original wind power output is carried out. The confidence level is selected as 0.93.…”

Hybrid attention-based temporal convolutional bidirectional LSTM approach for wind speed interval prediction

“…Adjusting the nonlinear convergence factor, as well as incorporating adaptive inertia weights and a chaos search technique, improved the IWOA’s convergence speed and accuracy. The authors of Zhang et al ( 2019 ) employed a multi-objective interval methods that rely on the conditional copula function, in which they completely exploited the correlations between variables to increase prediction accuracy without relying on an assumed probability distribution function. In Heydari et al ( 2021 ), a wind power producer (WPP) in a competitive power market is given an interval prediction algorithm based on a new bidding technique based on optimal scenario making.…”