现代电力2024，Vol.41Issue(1)：65-71,7.DOI:10.19725/j.cnki.1007-2322.2022.0180
基于门控循环神经网络的边缘服务中心风光荷组合预测方法
Combined Prediction Method for Wind-photovoltaic-load in Edge Service Center Based on ARIMA-GRU
欧阳含熠 1张立梅 1白牧可2
作者信息
- 1. 河北农业大学信息科学与技术学院,河北省保定市 071000
- 2. 中国电力科学研究院有限公司,北京市海淀区 100192
- 折叠
摘要
Abstract
Edge computing is extensively concerned by the en-ergy industry because of the advantages of fast data processing,low cost and high real-time,and the prediction on the edge server is helpful for the refined management and control of en-ergy.For this reason,in allusion to the limitations of edge ser-vice resources,based on difference autoregressive integrated moving average(ARIMA)model and gated recurrent unit(GRU)neural network a combined prediction method of wind,light and load was proposed.Firstly,the ARIMA was used to extract the linear characteristics of source and load,and through fitting linear characteristics with the true value the residual with nonlinear features was obtained.Secondly,taking the residual as the training dataset of GRU a prediction model was estab-lished,and then leading in the pruning and quantification meth-od the GRU model was optimized and compressed to reduce the size of the prediction model to suit the deploy of edge serv-ers.Results of lots of simulation examples show that the con-structed GRU compression model possesses the features of small scale and high prediction accuracy,so it is suitable to the deployment and application of edge servers.关键词
风光荷/边缘服务器/门控循环单元/神经网络/ARIMA/组合预测Key words
wind photovoltaic load/edge serve/GRU/neural network/ARIMA/combined prediction分类
信息技术与安全科学引用本文复制引用
欧阳含熠,张立梅,白牧可..基于门控循环神经网络的边缘服务中心风光荷组合预测方法[J].现代电力,2024,41(1):65-71,7.
