LAPSE:2023.32202
Published Article

LAPSE:2023.32202
How to Predict Energy Consumption in BRICS Countries?
April 20, 2023
Abstract
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.
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.
Record ID
Keywords
BRICS, energy consumption, forecasting accuracy, GM (1, 1), fractional-order, GREY
Subject
Suggested Citation
Khan AM, Osińska M. How to Predict Energy Consumption in BRICS Countries?. (2023). LAPSE:2023.32202
Author Affiliations
Journal Name
Energies
Volume
14
Issue
10
First Page
2749
Year
2021
Publication Date
2021-05-11
ISSN
1996-1073
Version Comments
Original Submission
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PII: en14102749, Publication Type: Journal Article
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LAPSE:2023.32202
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https://doi.org/10.3390/en14102749
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Apr 20, 2023
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Apr 20, 2023
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