Forecast of Coal Demand in Shanxi Province Based on GA—LSSVM under Multiple Scenarios

Yujing Liu (), Ruoyun Du and Dongxiao Niu
Additional contact information
Yujing Liu: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Ruoyun Du: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Dongxiao Niu: School of Economics and Management, North China Electric Power University, Beijing 102206, China

Energies, 2022, vol. 15, issue 17, 1-16

Abstract: Under the “carbon peaking and carbon neutrality” goal, Shanxi Province adjusts the power supply structure and promotes the development of a high proportion of new energy, which has a certain impact on the demand for thermal coal. Therefore, constructing a reasonable forecasting model for thermal coal demand can play a role in stabilizing coal supply and demand. This paper analyzes various factors related to coal demand, and uses Pearson coefficient to screen out six variables with strong correlation. Then, based on the scenario analysis method, combined with the “14th Five-Year Plan” of Shanxi Province, different scenarios of economic development and carbon emission reduction development are set. Finally, a multi-scenario GA–LSSVM forecasting model of thermal coal demand in Shanxi Province is constructed, and the future development trend of thermal coal demand in Shanxi Province is predicted. The results show that the demand for thermal coal is the largest in the mode of high-speed economic development and low emission reduction, and the demand for thermal coal is the lowest in the mode of low-speed economic development and strong emission reduction, which provides a scientific basis for the implementation of Shanxi Province’s thermal coal supply policy.

Keywords: coal demand forecast; scenario analysis; least squares support vector machine; genetic algorithm optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/17/6475/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/17/6475/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:17:p:6475-:d:906904

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().