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A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

Author

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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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    1. Taner, Tolga & Sivrioglu, Mecit, 2015. "Energy–exergy analysis and optimisation of a model sugar factory in Turkey," Energy, Elsevier, vol. 93(P1), pages 641-654.
    2. Mojtaba Bahmani & Mehdi Nejati & Amin GhasemiNejad & Fateme Nazari Robati & Mehrdad Lashkary & Naeeme Amani Zarin, 2021. "A Novel Hybrid Approach Based on BAT Algorithm with Artificial Neural Network to Forecast Iran’s Oil Consumption," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-9, February.
    3. Topal, Huseyin & Taner, Tolga & Naqvi, Syed Arslan Hassan & Altınsoy, Yelda & Amirabedin, Ehsan & Ozkaymak, Mehmet, 2017. "Exergy analysis of a circulating fluidized bed power plant co-firing with olive pits: A case study of power plant in Turkey," Energy, Elsevier, vol. 140(P1), pages 40-46.
    4. Ünler, Alper, 2008. "Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025," Energy Policy, Elsevier, vol. 36(6), pages 1937-1944, June.
    5. Azadeh, A. & Saberi, M. & Seraj, O., 2010. "An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran," Energy, Elsevier, vol. 35(6), pages 2351-2366.
    6. Taner, Tolga, 2018. "Energy and exergy analyze of PEM fuel cell: A case study of modeling and simulations," Energy, Elsevier, vol. 143(C), pages 284-294.
    7. Jaromir Vrbka, 2020. "The use of neural networks to determine value based drivers for SMEs operating in the rural areas of the Czech Republic," Oeconomia Copernicana, Institute of Economic Research, vol. 11(2), pages 325-346, June.
    8. Taner, Tolga & Sivrioglu, Mecit, 2017. "A techno-economic & cost analysis of a turbine power plant: A case study for sugar plant," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 722-730.
    9. Assareh, E. & Behrang, M.A. & Assari, M.R. & Ghanbarzadeh, A., 2010. "Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran," Energy, Elsevier, vol. 35(12), pages 5223-5229.
    10. Marek Vochozka & Jaromir Vrbka & Petr Suler, 2020. "Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    11. Zhang, Ming & Mu, Hailin & Li, Gang & Ning, Yadong, 2009. "Forecasting the transport energy demand based on PLSR method in China," Energy, Elsevier, vol. 34(9), pages 1396-1400.
    12. Azadeh, A. & Ghaderi, S.F. & Sohrabkhani, S., 2008. "A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran," Energy Policy, Elsevier, vol. 36(7), pages 2637-2644, July.
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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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