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Energy Sources, Part A: Recovery, Utilization, and Environmental Effects Volume 33, 2011 - Issue 14
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Original Articles

Forecasting of Coal Consumption Using an Artificial Neural Network and Comparison with Various Forecasting Techniques

S. Jebaraj Department of Mechanical Engineering, Dr.G.U. Pope College of Engineering, Sawyerpuram, Tuticorin District, India
,
S. Iniyan Department of Mechanical Engineering, Anna University, Chennai, India
&
R. Goic Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Split, Croatia
Pages 1305-1316 | Received 29 Jun 2009, Accepted 08 Oct 2009, Published online: 29 Apr 2011

Citations (15)

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S. Amosedinakaran, K. Mala, A. Bhuvanesh, S. Kannan & M. Karuppasamy Pandian. (2023) Energy Source Expansion Planning for a Real-world Deregulated Electric Utility Considering Emission. IETE Journal of Research 69:8, pages 5238-5249.
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K.J. Sreekanth, S. Jayaraj & N. Sudarsan. (2016) LP modelling for micro level domestic energy planning. Energy Sources, Part B: Economics, Planning, and Policy 11:4, pages 335-340.
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L. Xu, Y. Cui, Y. Song & X. Ma. (2015) The Application of an Improved BPNN Model to Coal Pyrolysis. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 37:16, pages 1805-1812.
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Articles from other publishers (12)

Guodang Zhao, Xuyang Guo, Xin Wang & Dezhi Zheng. (2023) Using a Novel Fractal-Time-Series Prediction Model to Predict Coal Consumption. Discrete Dynamics in Nature and Society 2023, pages 1-11.
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Arnab Mitra, Arnav Jain, Avinash Kishore & Pravin Kumar. (2022) A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach. Operations Research Forum 3:4.
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Dariusz Fuksa. (2021) A Method for Assessing the Impact of Changes in Demand for Coal on the Structure of Coal Grades Produced by Mines. Energies 14:21, pages 7111.
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Jinyuan Liu, Shouxi Wang, Nan Wei, Xi Chen, Hanyu Xie & Jie Wang. (2021) Natural gas consumption forecasting: A discussion on forecasting history and future challenges. Journal of Natural Gas Science and Engineering 90, pages 103930.
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Ali Roozbeh Nia, Anjali Awasthi & Nadia Bhuiyan. (2021) Industry 4.0 and demand forecasting of the energy supply chain: A literature review. Computers & Industrial Engineering 154, pages 107128.
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Devesh Kumar, Kritika Karwasra & Gunjan Soni. (2020) Bibliometric analysis of artificial neural network applications in materials and engineering. Materials Today: Proceedings 28, pages 1629-1634.
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Yan Li. (2019) Prediction of energy consumption: Variable regression or time series? A case in China. Energy Science & Engineering 7:6, pages 2510-2518.
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Nan Wei, Changjun Li, Xiaolong Peng, Fanhua Zeng & Xinqian Lu. (2019) Conventional models and artificial intelligence-based models for energy consumption forecasting: A review. Journal of Petroleum Science and Engineering 181, pages 106187.
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Weslei Gomes de Sousa, Elis Regina Pereira de Melo, Paulo Henrique De Souza Bermejo, Rafael Araújo Sousa Farias & Adalmir Oliveira Gomes. (2019) How and where is artificial intelligence in the public sector going? A literature review and research agenda. Government Information Quarterly 36:4, pages 101392.
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Yanbin Li & Zhen Li. (2019) Forecasting of Coal Demand in China Based on Support Vector Machine Optimized by the Improved Gravitational Search Algorithm. Energies 12:12, pages 2249.
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Bing-Bing Li, Qiao-Mei Liang & Jin-Cheng Wang. (2015) A comparative study on prediction methods for China's medium- and long-term coal demand. Energy 93, pages 1671-1683.
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E.T. Lau, Q. Yang, A.B. Forbes, P. Wright & V.N. Livina. (2014) Modelling carbon emissions in electric systems. Energy Conversion and Management 80, pages 573-581.
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