The large-scale utilization of wind energy brings severe challenges to the dispatching operation of power systems. Currently, the probability density prediction method combining quantile regression neural network (QRNN) with Epanechnikov kernel function is an excellent algorithm for wind power prediction, which can give the comprehensive probability distribution of future wind power and effectively quantify the uncertainty of wind power generation. However, existing probability density prediction methods process data sequentially in different quantiles, and computational time costs multiply with the increase of training data. It affects the practicality of the probability density prediction method. To overcome this issue, this paper proposes a multi-core parallel quantile regression neural network (MPQRNN) based on parallel master–slave (MS) model. The algorithm divides the complex prediction tasks at all quantiles into multiple parallel sub-tasks, which are independently run on different cores, so that performance advantages of the multi-core CPU can be fully utilized for improving the computational efficiency of the joint operation model. We compare four different scale sample sets under different process numbers. The influence of different CPU core numbers on the parallel performance of MPQRNN are analyzed by the algorithmic nature of speedup and parallel efficiency. To demonstrate the effectiveness of the proposed model, comparative experiments of other four traditional models are carried out on data sets. The simulation results demonstrate that the MPQRNN can not only improve the training efficiency of QRNN, but also obtain precise results of wind power forecasting, showing potential value and utility for complex power system.

风能的大规模利用给电力系统的调度运行带来了严峻的挑战。目前，将分位数回归神经网络（QRNN）与Epanechnikov核函数相结合的概率密度预测方法是一种出色的风电预测算法，可以给出未来风电的综合概率分布，并有效地量化风电发电的不确定性。但是，现有的概率密度预测方法以不同的分位数顺序地处理数据，并且随着训练数据的增加，计算时间成本成倍增加。它影响了概率密度预测方法的实用性。为了克服这个问题，本文提出了一种基于并行主从（MS）模型的多核并行分位数回归神经网络（MPQRNN）。该算法将所有分位数的复杂预测任务划分为多个并行子任务，分别在不同的内核上运行，从而可以充分利用多核CPU的性能优势，从而提高联合运算模型的计算效率。我们在不同的过程编号下比较了四个不同规模的样本集。通过加速和并行效率的算法特性，分析了不同CPU内核数量对MPQRNN并行性能的影响。为了证明所提出模型的有效性，在数据集上进行了其他四个传统模型的对比实验。仿真结果表明，MPQRNN不仅可以提高QRNN的训练效率，而且可以获得精确的风电预测结果，