计算机应用与软件2025，Vol.42Issue(6)：127-135,209,10.DOI:10.3969/j.issn.1000-386x.2025.06.017
基于深度混合密度网络的住宅负荷概率预测
PROBABILITY PREDICTION OF RESIDENTIAL LOAD BASED ON DEEP MIXED DENSITY NETWORK
摘要
Abstract
In order to solve the problem of uncertainty and improve the prediction accuracy,a probability prediction of residential load based on deep mixed density network is proposed.An end-to-end convolution neural network combined with gated recursive unit was designed.A loss function was reconstructed to prevent large errors caused by indirect structure propagation and improve the calculation efficiency.Further,the designed depth model was combined into the hybrid density network to directly predict the probability density function.The case results show that compared with other methods,this method has certain advantages in residential load probability prediction.关键词
深度混合密度网络/住宅负荷/概率密度/门控循环单元Key words
Deep mixed density network/Residential load/Probability density/Gated circulation unit分类
信息技术与安全科学引用本文复制引用
张威,张晓丽,刘影,彭鑫霞,王慧楠..基于深度混合密度网络的住宅负荷概率预测[J].计算机应用与软件,2025,42(6):127-135,209,10.基金项目
国网冀北电力有限公司科技项目(5201041900VX). （5201041900VX）
