Probability density forecasting of wind power based on multi-core parallel quantile regression neural network

Abstract

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.