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A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

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  • Mahdis sadat Jalaee

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

  • Alireza Shakibaei

    (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)

  • 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

Coal as a fossil and non-renewable fuel is one of the most valuable energy minerals in the world with the largest volume reserves. Artificial neural networks (ANN), despite being one of the highest breakthroughs in the field of computational intelligence, has some significant disadvantages, such as slow training, susceptibility to falling into a local optimal points, sensitivity of initial weights, and bias. To overcome these shortcomings, this study presents an improved ANN structure, that is optimized by a proposed hybrid method. The aim of this study is to propose a novel hybrid method for predicting coal consumption in Iran based on socio-economic variables using the bat and grey wolf optimization algorithm with an artificial neural network (BGWAN). For this purpose, data from 1981 to 2019 have been used for modelling and testing the method. The available data are partly used to find the optimal or near-optimal values of the weighting parameters (1980–2014) and partly to test the model (2015–2019). The performance of the BGWAN is evaluated by mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), standard deviation error (STD), and correlation coefficient (R^2) between the output of the method and the actual dataset. The result of this study showed that BGWAN performance was excellent and proved its efficiency as a useful and reliable tool for monitoring coal consumption or energy demand in Iran.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:14:p:7612-:d:590249
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Reza Derakhshani & Mojtaba Zaresefat & Vahid Nikpeyman & Amin GhasemiNejad & Shahram Shafieibafti & Ahmad Rashidi & Majid Nemati & Amir Raoof, 2023. "Machine Learning-Based Assessment of Watershed Morphometry in Makran," Land, MDPI, vol. 12(4), pages 1-19, March.

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