How to Predict Energy Consumption in BRICS Countries?
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...
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MDPI AG
2021-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/10/2749 |
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| author | Atif Maqbool Khan Magdalena Osińska |
| author_facet | Atif Maqbool Khan Magdalena Osińska |
| author_sort | Atif Maqbool Khan |
| collection | DOAJ |
| description | 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 (<i>FGM</i>) with different order parameters. This study contributes to the literature by comparing the forecasting accuracy and the predictive ability of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>F</mi><mi>G</mi><mi>M</mi><mfenced><mrow><mn>1</mn><mo>,</mo><mn>1</mn></mrow></mfenced></mrow></semantics></math></inline-formula> with traditional ones, like standard <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>G</mi><mi>M</mi><mfenced><mrow><mn>1</mn><mo>,</mo><mn>1</mn></mrow></mfenced></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>A</mi><mi>R</mi><mi>I</mi><mi>M</mi><mi>A</mi><mfenced><mrow><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mn>1</mn></mrow></mfenced></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>F</mi><mi>G</mi><mi>M</mi><mfenced><mrow><mn>1</mn><mo>,</mo><mn>1</mn></mrow></mfenced></mrow></semantics></math></inline-formula> for a specific range of order parameters and the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>A</mi><mi>R</mi><mi>I</mi><mi>M</mi><mi>A</mi><mfenced><mrow><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mn>1</mn></mrow></mfenced></mrow></semantics></math></inline-formula> model and the usefulness of both approaches for energy consumption efficient forecasting. |
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| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T11:31:33Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
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| series | Energies |
| spelling | doaj.art-4a7260c31eff4a10a01574fb407ee57e2023-11-21T19:11:50ZengMDPI AGEnergies1996-10732021-05-011410274910.3390/en14102749How to Predict Energy Consumption in BRICS Countries?Atif Maqbool Khan0Magdalena Osińska1Department of Economics, University Centre of Excellence, Interacting Minds, Societies, Environments, Nicolaus Copernicus University, 87-100 Toruń, PolandDepartment of Economics, Nicolaus Copernicus University, 87-100 Toruń, PolandBrazil, 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 (<i>FGM</i>) with different order parameters. This study contributes to the literature by comparing the forecasting accuracy and the predictive ability of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>F</mi><mi>G</mi><mi>M</mi><mfenced><mrow><mn>1</mn><mo>,</mo><mn>1</mn></mrow></mfenced></mrow></semantics></math></inline-formula> with traditional ones, like standard <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>G</mi><mi>M</mi><mfenced><mrow><mn>1</mn><mo>,</mo><mn>1</mn></mrow></mfenced></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>A</mi><mi>R</mi><mi>I</mi><mi>M</mi><mi>A</mi><mfenced><mrow><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mn>1</mn></mrow></mfenced></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>F</mi><mi>G</mi><mi>M</mi><mfenced><mrow><mn>1</mn><mo>,</mo><mn>1</mn></mrow></mfenced></mrow></semantics></math></inline-formula> for a specific range of order parameters and the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>A</mi><mi>R</mi><mi>I</mi><mi>M</mi><mi>A</mi><mfenced><mrow><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mn>1</mn></mrow></mfenced></mrow></semantics></math></inline-formula> model and the usefulness of both approaches for energy consumption efficient forecasting.https://www.mdpi.com/1996-1073/14/10/2749energy consumptionBRICSGM (1, 1), fractional-orderGREYforecasting accuracy |
| spellingShingle | Atif Maqbool Khan Magdalena Osińska How to Predict Energy Consumption in BRICS Countries? Energies energy consumption BRICS GM (1, 1), fractional-order GREY forecasting accuracy |
| title | How to Predict Energy Consumption in BRICS Countries? |
| title_full | How to Predict Energy Consumption in BRICS Countries? |
| title_fullStr | How to Predict Energy Consumption in BRICS Countries? |
| title_full_unstemmed | How to Predict Energy Consumption in BRICS Countries? |
| title_short | How to Predict Energy Consumption in BRICS Countries? |
| title_sort | how to predict energy consumption in brics countries |
| topic | energy consumption BRICS GM (1, 1), fractional-order GREY forecasting accuracy |
| url | https://www.mdpi.com/1996-1073/14/10/2749 |
| work_keys_str_mv | AT atifmaqboolkhan howtopredictenergyconsumptioninbricscountries AT magdalenaosinska howtopredictenergyconsumptioninbricscountries |