TY - JOUR
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.
KW - whale optimization algorithm
KW - adaptive neuro-fuzzy inference system
KW - climate change
KW - energy consumption
UR - http://www.scopus.com/inward/record.url?scp=85128098328&partnerID=8YFLogxK
U2 - 10.3390/en15072578
DO - 10.3390/en15072578
M3 - Article
SN - 1996-1073
VL - 15
SP - 1
EP - 14
JO - Energies
JF - Energies
IS - 7
M1 - 2578
ER -