摘要
城市燃气公司为防止出现储气量过剩或不足等问题,必须要对天然气负荷量进行预测。对此,提出一种基于小波变换和长短期记忆网络的深度学习模型(wavelet_LSTM),对未来一年的天然气日负荷量进行预测。在某市燃气公司真实数据集上的实验结果表明,该模型有较高的准确性,可以较好地满足城市燃气公司的业务需求。
In order to prevent problems such as excess or insufficient gas reserves,city gas companies must forecast the natural gas load.This paper proposes a deep learning model based on wavelet transform and long short term memory network(wavelet_LSTM)to predict the daily natural gas load in the coming year.The experimental results on a real data set of a city gas company show that this model has higher accuracy and can better meet the business needs of city gas companies.
作者
那幸仪
贾俊铖
赵晓筠
张莉
李凡长
Na Xingyi;Jia Juncheng;Zhao Xiaojun;Zhang Li;Li Fanzhang(School of Computer Science and Technology,Soochow University,Suzhou 215000,Jiangsu,China;Provincial Key Laboratory for Computer Information Processing Technology,Soochow University,Suzhou 215000,Jiangsu,China;Suzhou Gas Limited Liability Company,Suzhou 215000,Jiangsu,China)
出处
《计算机应用与软件》
北大核心
2021年第12期61-66,共6页
Computer Applications and Software
基金
江苏高校优势学科建设工程项目(PAPD)
中国博士后科学基金项目(2017M611905)
江苏省高等学校自然科学研究面上资助经费项目(17KJB520034)
苏州市科技项目(SS201701,SYSD20192152)。
关键词
长短期记忆网络
小波变换
天然气
负荷预测
Long short term memory
Wavelet transform
Natural gas
Load forecasting
