Forecasting Coal Consumption in India by 2030: Using Linear Modified Linear (MGM-ARIMA) and Linear Modified Nonlinear (BP-ARIMA) Combined Models

Shuyu Li, Xuan Yang and Rongrong Li
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Shuyu Li: School of Economics and Management, China University of Petroleum (East China), Qingdao 266580, China
Xuan Yang: School of Economics and Management, China University of Petroleum (East China), Qingdao 266580, China
Rongrong Li: School of Economics and Management, China University of Petroleum (East China), Qingdao 266580, China

Sustainability, 2019, vol. 11, issue 3, 1-19

Abstract: India’s coal consumption is closely related to greenhouse gas emissions and the balance of supply and demand in energy trading markets. Most existing research on India focuses on total energy, renewable energy and energy intensity. To fill this gap, this study used two single forecasting models: the metabolic grey model (MGM) and the Back-Pro-Pagation Network (BP) to make predictions. In addition, based on these two single models, this study also developed the ARIMA correction principle and derived two combined models: the metabolic grey model, the Autoregressive Integrated Moving Average model (MGM-ARIMA) and Back-Pro-Pagation Network; and the Autoregressive Integrated Moving Average model (BP-ARIMA). After fitting India’s coal consumption during 1995–2017, the average relative errors of the four models were 2.28%, 1.53%, 1.50% and 1.42% respectively. The forecast results show that coal consumption in India will continue to increase at an average annual rate of 2.5% during the period from 2018–2030.

Keywords: India’s coal consumption; Multiple forecasting models; ARIMA corrected principle; MGM-ARIMA; BP-ARIMA (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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