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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

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  • Mahdis sadat Jalaee

    (Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran)

  • Amin GhasemiNejad

    (Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran)

  • Sayyed Abdolmajid Jalaee

    (Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran)

  • Naeeme Amani Zarin

    (Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran)

  • Reza Derakhshani

    (Department of Geology, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran
    Department of Earth Sciences, Utrecht University, 3584 CB Utrecht, The Netherlands)

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 (R 2 ) 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 (R 2 ) 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.

Suggested Citation

  • Mahdis sadat Jalaee & Amin GhasemiNejad & Sayyed Abdolmajid Jalaee & Naeeme Amani Zarin & Reza Derakhshani, 2022. "A Novel Hybrid Artificial Intelligence Approach to the Future of Global Coal Consumption Using Whale Optimization Algorithm and Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 15(7), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2578-:d:785279
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    References listed on IDEAS

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    1. Monowar Hossain & Saad Mekhilef & Firdaus Afifi & Laith M Halabi & Lanre Olatomiwa & Mehdi Seyedmahmoudian & Ben Horan & Alex Stojcevski, 2018. "Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-31, April.
    2. Peilin Liu & Wenhao Leng & Wei Fang, 2013. "Training ANFIS Model with an Improved Quantum-Behaved Particle Swarm Optimization Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-10, June.
    3. Sayyed Abdolmajid Jalaee & Amin GhasemiNejad & Mehrdad Lashkary & Maryam Rezaee Jafari, 2019. "Forecasting Iran’s Energy Demand Using Cuckoo Optimization Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-8, September.
    4. Mahdis sadat Jalaee & Alireza Shakibaei & Amin GhasemiNejad & Sayyed Abdolmajid Jalaee & Reza Derakhshani, 2021. "A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran," Sustainability, MDPI, vol. 13(14), pages 1-16, July.
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