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ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Sustainable Energy Systems
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1443597
This article is part of the Research Topic Urban Energy System Planning, Operation, and Control with High Efficiency and Low Carbon Goals View all 26 articles

Monthly Industrial Added Value Monitoring Model with Multi-Source Big Data

Provisionally accepted
Zhanjie Liu Zhanjie Liu 1Shifeng Fan Shifeng Fan 2Jiaqi YUAN Jiaqi YUAN 1*Biao Yang Biao Yang 1Hong Tan Hong Tan 3
  • 1 State Grid Energy Research Institute (SGCC), Beijing, China
  • 2 State Grid Corporation of China (SGCC), Beijing, Beijing Municipality, China
  • 3 College of Electrical Engineering and New Energy, China Three Gorges University, Yichang, Hebei Province, China

The final, formatted version of the article will be published soon.

    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. 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. The forecasting accuracy on four provincial companies has reached over 90%. The final forecast results can be submitted to government departments to provide suggestions for guiding macroeconomic development.

    Keywords: Industrial added value, Power data, grounded theory, Expert interviews, Big data technology

    Received: 04 Jun 2024; Accepted: 03 Jul 2024.

    Copyright: © 2024 Liu, Fan, YUAN, Yang and Tan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jiaqi YUAN, State Grid Energy Research Institute (SGCC), Beijing, China

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