A Novel Hybrid Artificial Intelligence Approach to the Future of Global Coal Consumption Using Whale Optimization Algorithm and Adaptive Neuro-Fuzzy Inference System

Mahdis sadat Jalaee; Amin GhasemiNejad; Sayyed Abdolmajid Jalaee; Naeeme Amani zarin; Reza Derakhshani

doi:10.3390/en15072578

A Novel Hybrid Artificial Intelligence Approach to the Future of Global Coal Consumption Using Whale Optimization Algorithm and Adaptive Neuro-Fuzzy Inference System

Mahdis sadat Jalaee, Amin GhasemiNejad, Sayyed Abdolmajid Jalaee, Naeeme Amani zarin, Reza Derakhshani^*

^*Corresponding author for this work

Shahid Bahonar University of Kerman

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Energy has become an integral part of our society and global economic development in the twenty-first century. Despite tremendous technological advancements, fossil fuels (coal, natural gas, and oil) continue to be the world’s primary source of energy. Global energy scenarios indicate a change in coal consumption trends in the future, which in turn will have commercial, geopolitical, and environmental consequences. We investigated coal consumption up to 2030 using a new hybrid method of WOANFIS (whale optimization algorithm and adaptive neuro-fuzzy inference system). The WOANFIS method’s performance was assessed by the MSE (Mean Squared Error), MAE (Mean Absolute Error), STD (error standard deviation), RMSE (Root Mean Squared Error), and coefficient of correlation (R²) among the real dataset and the WOANFIS result. For the prediction of global coal consumption, the proposed WOANFIS had the best MAE, RMSE, and correlation coefficient (R²) values, which were 0.00113, 0.0047, and 0.98, respectively. Lastly, future global coal consumption was predicted up to 2030 by WOANFIS. Following 150 years of coal dominance, the results demonstrate that WOANFIS is a suitable method for estimating worldwide coal consumption, which makes it possible to plan for the transition away from coal.

Original language	English
Article number	2578
Pages (from-to)	1-14
Journal	Energies
Volume	15
Issue number	7
DOIs	https://doi.org/10.3390/en15072578
Publication status	Published - 1 Apr 2022

Bibliographical note

Funding Information:
This article is the result of a joint research study of Shahid Bahonar University of Kerman, Iran, and Utrecht University, the Netherlands. The authors would like to thank the Department of Management and Economics of Shahid Bahonar University of Kerman for providing the necessary facilities and active cooperation in this research as well as the Department of Earth Sciences at Utrecht University for their research support.

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

whale optimization algorithm
adaptive neuro-fuzzy inference system
climate change
energy consumption

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3390/en15072578Licence: CC BY

energies-15-02578Final published version, 2.03 MBLicence: CC BY

Cite this

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title = "A Novel Hybrid Artificial Intelligence Approach to the Future of Global Coal Consumption Using Whale Optimization Algorithm and Adaptive Neuro-Fuzzy Inference System",

abstract = "Energy has become an integral part of our society and global economic development in the twenty-first century. Despite tremendous technological advancements, fossil fuels (coal, natural gas, and oil) continue to be the world{\textquoteright}s primary source of energy. Global energy scenarios indicate a change in coal consumption trends in the future, which in turn will have commercial, geopolitical, and environmental consequences. We investigated coal consumption up to 2030 using a new hybrid method of WOANFIS (whale optimization algorithm and adaptive neuro-fuzzy inference system). The WOANFIS method{\textquoteright}s performance was assessed by the MSE (Mean Squared Error), MAE (Mean Absolute Error), STD (error standard deviation), RMSE (Root Mean Squared Error), and coefficient of correlation (R2) among the real dataset and the WOANFIS result. For the prediction of global coal consumption, the proposed WOANFIS had the best MAE, RMSE, and correlation coefficient (R2) values, which were 0.00113, 0.0047, and 0.98, respectively. Lastly, future global coal consumption was predicted up to 2030 by WOANFIS. Following 150 years of coal dominance, the results demonstrate that WOANFIS is a suitable method for estimating worldwide coal consumption, which makes it possible to plan for the transition away from coal.",

keywords = "whale optimization algorithm, adaptive neuro-fuzzy inference system, climate change, energy consumption",

author = "Jalaee, {Mahdis sadat} and Amin GhasemiNejad and Jalaee, {Sayyed Abdolmajid} and {Amani zarin}, Naeeme and Reza Derakhshani",

note = "Funding Information: This article is the result of a joint research study of Shahid Bahonar University of Kerman, Iran, and Utrecht University, the Netherlands. The authors would like to thank the Department of Management and Economics of Shahid Bahonar University of Kerman for providing the necessary facilities and active cooperation in this research as well as the Department of Earth Sciences at Utrecht University for their research support. Publisher Copyright: {\textcopyright} 2022 by the authors. Licensee MDPI, Basel, Switzerland.",

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doi = "10.3390/en15072578",

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A Novel Hybrid Artificial Intelligence Approach to the Future of Global Coal Consumption Using Whale Optimization Algorithm and Adaptive Neuro-Fuzzy Inference System. / Jalaee, Mahdis sadat; GhasemiNejad, Amin; Jalaee, Sayyed Abdolmajid et al.
In: Energies, Vol. 15, No. 7, 2578, 01.04.2022, p. 1-14.

Research output: Contribution to journal › Article › Academic › peer-review

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T1 - A Novel Hybrid Artificial Intelligence Approach to the Future of Global Coal Consumption Using Whale Optimization Algorithm and Adaptive Neuro-Fuzzy Inference System

AU - Jalaee, Mahdis sadat

AU - GhasemiNejad, Amin

AU - Jalaee, Sayyed Abdolmajid

AU - Amani zarin, Naeeme

AU - Derakhshani, Reza

N1 - Funding Information: This article is the result of a joint research study of Shahid Bahonar University of Kerman, Iran, and Utrecht University, the Netherlands. The authors would like to thank the Department of Management and Economics of Shahid Bahonar University of Kerman for providing the necessary facilities and active cooperation in this research as well as the Department of Earth Sciences at Utrecht University for their research support. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2022/4/1

Y1 - 2022/4/1

N2 - Energy has become an integral part of our society and global economic development in the twenty-first century. Despite tremendous technological advancements, fossil fuels (coal, natural gas, and oil) continue to be the world’s primary source of energy. Global energy scenarios indicate a change in coal consumption trends in the future, which in turn will have commercial, geopolitical, and environmental consequences. We investigated coal consumption up to 2030 using a new hybrid method of WOANFIS (whale optimization algorithm and adaptive neuro-fuzzy inference system). The WOANFIS method’s performance was assessed by the MSE (Mean Squared Error), MAE (Mean Absolute Error), STD (error standard deviation), RMSE (Root Mean Squared Error), and coefficient of correlation (R2) among the real dataset and the WOANFIS result. For the prediction of global coal consumption, the proposed WOANFIS had the best MAE, RMSE, and correlation coefficient (R2) values, which were 0.00113, 0.0047, and 0.98, respectively. Lastly, future global coal consumption was predicted up to 2030 by WOANFIS. Following 150 years of coal dominance, the results demonstrate that WOANFIS is a suitable method for estimating worldwide coal consumption, which makes it possible to plan for the transition away from coal.

AB - Energy has become an integral part of our society and global economic development in the twenty-first century. Despite tremendous technological advancements, fossil fuels (coal, natural gas, and oil) continue to be the world’s primary source of energy. Global energy scenarios indicate a change in coal consumption trends in the future, which in turn will have commercial, geopolitical, and environmental consequences. We investigated coal consumption up to 2030 using a new hybrid method of WOANFIS (whale optimization algorithm and adaptive neuro-fuzzy inference system). The WOANFIS method’s performance was assessed by the MSE (Mean Squared Error), MAE (Mean Absolute Error), STD (error standard deviation), RMSE (Root Mean Squared Error), and coefficient of correlation (R2) among the real dataset and the WOANFIS result. For the prediction of global coal consumption, the proposed WOANFIS had the best MAE, RMSE, and correlation coefficient (R2) values, which were 0.00113, 0.0047, and 0.98, respectively. Lastly, future global coal consumption was predicted up to 2030 by WOANFIS. Following 150 years of coal dominance, the results demonstrate that WOANFIS is a suitable method for estimating worldwide coal consumption, which makes it possible to plan for the transition away from coal.

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KW - adaptive neuro-fuzzy inference system

KW - climate change

KW - energy consumption

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DO - 10.3390/en15072578

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JO - Energies

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M1 - 2578

ER -

A Novel Hybrid Artificial Intelligence Approach to the Future of Global Coal Consumption Using Whale Optimization Algorithm and Adaptive Neuro-Fuzzy Inference System

Abstract

Bibliographical note

Keywords

UN SDGs

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