Development of Grey Machine Learning Models for Forecasting of Energy Consumption, Carbon Emission and Energy Generation for the Sustainable Development of Society

Akash Saxena, Ramadan A. Zeineldin and Ali Wagdy Mohamed ()
Additional contact information
Akash Saxena: Department of Electrical Engineering, Central University of Haryana, Mahendergarh 123031, India
Ramadan A. Zeineldin: Deanship of Scientific Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Ali Wagdy Mohamed: Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt

Mathematics, 2023, vol. 11, issue 6, 1-13

Abstract: Energy is an important denominator for evaluating the development of any country. Energy consumption, energy production and steps towards obtaining green energy are important factors for sustainable development. With the advent of forecasting technologies, these factors can be accessed earlier, and the planning path for sustainable development can be chalked out. Forecasting technologies pertaining to grey systems are in the spotlight due to the fact that they do not require many data points. In this work, an optimized model with grey machine learning architecture of a polynomial realization was employed to predict power generation, power consumption and CO 2 emissions. A nonlinear kernel was taken and optimized with a recently published algorithm, the augmented crow search algorithm (ACSA), for prediction. It was found that as compared to conventional grey models, the proposed framework yields better results in terms of accuracy.

Keywords: grey model; polynomial based kernel; augmented crow search algorithm; optimization; soft computing; forecasting; optimized fractional overhead power term polynomial grey model (OFOPGM) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/6/1505/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/6/1505/ (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:jmathe:v:11:y:2023:i:6:p:1505-:d:1102089

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

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