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Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model

Author

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  • Feng Jiang

    (School of Economic and Management, China University of Petroleum (East China), Qingdao 266580, China)

  • Xue Yang

    (School of Economic and Management, China University of Petroleum (East China), Qingdao 266580, China)

  • Shuyu Li

    (School of Economic and Management, China University of Petroleum (East China), Qingdao 266580, China)

Abstract

Better prediction of energy demand is of vital importance for developing countries to develop effective energy strategies to improve energy security, partly because those countries’ energy demands are increasing rapidly. In this work, metabolic grey model (MGM), autoregressive integrated moving average (ARIMA), MGM-ARIMA, and back propagation neural network (BP) are adopted to forecast energy demand in India, the third largest energy consumer in the world after China and the USA. The average relative errors between the actual and simulated value are 1.31% (MGM), 1.07%, 0.92% (MGM-ARIMA), and 0.39% (BP). The high prediction accuracy indicates that the prediction result is effective. The result shows that India’s energy consumption will increase by 4.75% a year in the next 14 years. Compared with the 5.1% per year on average in 1995–2016, India’s energy consumption will still continue its steady growth at about 5% growth from 2017 to 2030.

Suggested Citation

  • Feng Jiang & Xue Yang & Shuyu Li, 2018. "Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model," Sustainability, MDPI, vol. 10(7), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:7:p:2225-:d:155073
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    as
    1. Hamzacebi, Coskun & Es, Huseyin Avni, 2014. "Forecasting the annual electricity consumption of Turkey using an optimized grey model," Energy, Elsevier, vol. 70(C), pages 165-171.
    2. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
    3. Broadberry, Stephen & Custodis, Johann & Gupta, Bishnupriya, 2015. "India and the great divergence: An Anglo-Indian comparison of GDP per capita, 1600–1871," Explorations in Economic History, Elsevier, vol. 55(C), pages 58-75.
    4. Bhattacharya, S.C., 2015. "Wood energy in India: Status and prospects," Energy, Elsevier, vol. 85(C), pages 310-316.
    5. Wang, Qiang & Li, Rongrong, 2016. "Journey to burning half of global coal: Trajectory and drivers of China׳s coal use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 341-346.
    6. Wang, Qiang & Jiang, Xue-ting & Li, Rongrong, 2017. "Comparative decoupling analysis of energy-related carbon emission from electric output of electricity sector in Shandong Province, China," Energy, Elsevier, vol. 127(C), pages 78-88.
    7. Gupta, C.L. & Rao, K.Usha & Vasudevaraju, V.A., 1980. "Domestic energy consumption in India (Pondicherry region)," Energy, Elsevier, vol. 5(12), pages 1213-1222.
    8. Ardakani, F.J. & Ardehali, M.M., 2014. "Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types," Energy, Elsevier, vol. 65(C), pages 452-461.
    9. Yu, Feng & Xu, Xiaozhong, 2014. "A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network," Applied Energy, Elsevier, vol. 134(C), pages 102-113.
    10. Kalyani, Khanjan Ajaybhai & Pandey, Krishan K., 2014. "Waste to energy status in India: A short review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 113-120.
    11. Wang, Qiang & Li, Rongrong, 2016. "Drivers for energy consumption: A comparative analysis of China and India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 954-962.
    12. Kriechbaumer, Thomas & Angus, Andrew & Parsons, David & Rivas Casado, Monica, 2014. "An improved wavelet–ARIMA approach for forecasting metal prices," Resources Policy, Elsevier, vol. 39(C), pages 32-41.
    13. Shahbaz, Muhammad & Mallick, Hrushikesh & Mahalik, Mantu Kumar & Sadorsky, Perry, 2016. "The role of globalization on the recent evolution of energy demand in India: Implications for sustainable development," Energy Economics, Elsevier, vol. 55(C), pages 52-68.
    14. Xiong, Ping-ping & Dang, Yao-guo & Yao, Tian-xiang & Wang, Zheng-xin, 2014. "Optimal modeling and forecasting of the energy consumption and production in China," Energy, Elsevier, vol. 77(C), pages 623-634.
    15. Nana Geng & Yong Zhang & Yixiang Sun & Yunjian Jiang & Dandan Chen, 2015. "Forecasting China’s Annual Biofuel Production Using an Improved Grey Model," Energies, MDPI, vol. 8(10), pages 1-20, October.
    16. Unknown, 2016. "Energy for Sustainable Development," Conference Proceedings 253270, Guru Arjan Dev Institute of Development Studies (IDSAsr).
    17. Franco, Sainu & Mandla, Venkata Ravibabu & Ram Mohan Rao, K., 2017. "Urbanization, energy consumption and emissions in the Indian context A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 898-907.
    18. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    19. Shuyu Li & Xue Yang & Rongrong Li, 2018. "Forecasting China’s Coal Power Installed Capacity: A Comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models," Sustainability, MDPI, vol. 10(2), pages 1-15, February.
    20. Nejat, Payam & Jomehzadeh, Fatemeh & Taheri, Mohammad Mahdi & Gohari, Mohammad & Abd. Majid, Muhd Zaimi, 2015. "A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 843-862.
    21. Mustafa Akpinar & Nejat Yumusak, 2016. "Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods," Energies, MDPI, vol. 9(9), pages 1-17, September.
    22. Wang, Qiang & Li, Rongrong, 2017. "Decline in China's coal consumption: An evidence of peak coal or a temporary blip?," Energy Policy, Elsevier, vol. 108(C), pages 696-701.
    23. Sen, Parag & Roy, Mousumi & Pal, Parimal, 2016. "Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization," Energy, Elsevier, vol. 116(P1), pages 1031-1038.
    24. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    25. Shuyu Li & Rongrong Li, 2017. "Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model," Sustainability, MDPI, vol. 9(7), pages 1-19, July.
    26. de Oliveira, Erick Meira & Cyrino Oliveira, Fernando Luiz, 2018. "Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods," Energy, Elsevier, vol. 144(C), pages 776-788.
    27. Tripathi, Lata & Mishra, A.K. & Dubey, Anil Kumar & Tripathi, C.B. & Baredar, Prashant, 2016. "Renewable energy: An overview on its contribution in current energy scenario of India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 226-233.
    28. Mohanty, Sthitapragyan & Patra, Prashanta K. & Sahoo, Sudhansu S. & Mohanty, Asit, 2017. "Forecasting of solar energy with application for a growing economy like India: Survey and implication," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 539-553.
    29. Wang, Qiang & Chen, Xi, 2015. "Energy policies for managing China’s carbon emission," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 470-479.
    30. Wang, Qiang & Li, Rongrong, 2016. "Natural gas from shale formation: A research profile," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1-6.
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