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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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