Monthly industrial added value monitoring model with multi-source big data

被引:0
作者
Liu, Zhanjie [1 ]
Fan, Shifeng [2 ]
Yuan, Jiaqi [1 ]
Yang, Biao [1 ]
Tan, Hong [3 ]
机构
[1] State Grid Energy Res Inst Co Ltd, Beijing, Peoples R China
[2] State Grid Corp China, Beijing, Peoples R China
[3] China Three Gorges Univ, Coll Elect Engn & New Energy, Yichang, Peoples R China
关键词
industrial added value; power data; grounded theory; expert interviews; big data technology; ECONOMIC-GROWTH; UNCERTAINTY;
D O I
10.3389/fenrg.2024.1443597
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Introduction: With the increasing fluctuations in the current domestic and international economic situation and the rapid iteration of macroeconomic regulation and control demands, the inadequacy of the existing economic data statistical system in terms of agility has been exposed. It has become a primary task to closely track and accurately predict the domestic and international economic situation using effective tools and measures to compensate for the inadequate economic early warning system and promote stable and orderly industrial production.Methods: Against this background, this paper takes industrial added value as the forecasting object, uses electricity consumption to predict industrial added value, selects factors influencing industrial added value based on grounded theory, and constructs a big data forecasting model using a combination of "expert interviews + big data technology" for economic forecasting.Results: The forecasting accuracy on four provincial companies has reached over 90%.Discussion: The final forecast results can be submitted to government departments to provide suggestions for guiding macroeconomic development.
引用
收藏
页数:12
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