How to Predict Energy Consumption in BRICS Countries?

被引:17
|
作者
Khan, Atif Maqbool [1 ]
Osinska, Magdalena [2 ]
机构
[1] Nicolaus Copernicus Univ, Univ Ctr Excellence, Dept Econ, Interacting Minds,Soc,Environm, PL-87100 Torun, Poland
[2] Nicolaus Copernicus Univ, Dept Econ, PL-87100 Torun, Poland
关键词
energy consumption; BRICS; GM (1,1); fractional-order; GREY; forecasting accuracy; EXTREME LEARNING-MACHINE; NATURAL-GAS CONSUMPTION; ELECTRICITY CONSUMPTION; GREY MODEL; ECONOMIC-GROWTH; TIME-SERIES; CAUSAL RELATIONSHIP; N) MODEL; CHINA; DEMAND;
D O I
10.3390/en14102749
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Brazil, Russia, China, India, and the Republic of South Africa (BRICS) represent developing economies facing different energy and economic development challenges. The current study aims to predict energy consumption in BRICS at aggregate and disaggregate levels using the annual time series data set from 1992 to 2019 and to compare results obtained from a set of models. The time-series data are from the British Petroleum (BP-2019) Statistical Review of World Energy. The forecasting methodology bases on a novel Fractional-order Grey Model (FGM) with different order parameters. This study contributes to the literature by comparing the forecasting accuracy and the predictive ability of the FGM1,1 with traditional ones, like standard GM1,1 and ARIMA1,1,1 models. Moreover, it illustrates the view of BRICS's nexus of energy consumption at aggregate and disaggregates levels using the latest available data set, which will provide a reliable and broader perspective. The Diebold-Mariano test results confirmed the equal predictive ability of FGM1,1 for a specific range of order parameters and the ARIMA1,1,1 model and the usefulness of both approaches for energy consumption efficient forecasting.
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页数:21
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