Wind Power Probability Density Prediction Based on Quantile Regression Model of Dilated Causal Convolutional Neural Network

作     者：Yunhao Yang Heng Zhang Shurong Peng Sheng Su Bin Li 

作者机构：College of Computer Science and TechnologyZhejiang UniversityHangzhou 310058China State Grid Jining Electric Power CorporationJining 272100China School of Electrical&Information EngineeringChangsha University of Science&TechnologyChangsha 410114China 

出 版 物：《Chinese Journal of Electrical Engineering》 (中国电气工程学报（英文）)

年 卷 期：2023年第9卷第1期

页      面：120-128页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by the National Natural Science Foundation of China(51777015) the Research Foundation of Education Bureau of Hunan Province(20A021) 

主　　题：Dilated causal neural network nuclear density estimation wind power probability prediction quantile regression probability density distribution 

摘      要：Aiming at the wind power prediction problem,a wind power probability prediction method based on the quantile regression of a dilated causal convolutional neural network is *** the developed model,the Adam stochastic gradient descent technique is utilized to solve the cavity parameters of the causal convolutional neural network under different quantile conditions and obtain the probability density distribution of wind power at various times within the following 200 *** presented method can obtain more useful information than conventional point and interval ***,a prediction of the future complete probability distribution of wind power can be *** to the actual data forecast of wind power in the PJM network in the United States,the proposed probability density prediction approach can not only obtain more accurate point prediction results,it also obtains the complete probability density curve prediction results for wind *** with two other quantile regression methods,the developed technique can achieve a higher accuracy and smaller prediction interval range under the same confidence level.