Skip to main content
SpringerLink
Log in

Modeling, simulation, and prediction of global energy indices: a differential approach

  • Research Article
  • Published:
Frontiers in Energy Aims and scope Submit manuscript

Abstract

Modeling, simulation, and prediction of global energy indices remain veritable tools for econometric, engineering, analysis, and prediction of energy indices. Thus, this paper differentially modeled, simulated, and non-differentially predicated the global energy indices. The state-of-the-art of the research includes normalization of energy indices, generation of differential rate terms, and regression of rate terms against energy indices to generate coefficients and unexplained terms. On imposition of initial conditions, the solution to the system of linear differential equations was realized in a Matlab environment. There was a strong agreement between the simulated and the field data. The exact solutions are ideal for interpolative prediction of historic data. Furthermore, the simulated data were upgraded for extrapolative prediction of energy indices by introducing an innovative model, which is the synergy of deflated and inflated prediction factors. The innovative model yielded a trendy prediction data for energy consumption, gross domestic product, carbon dioxide emission and human development index. However, the oil price was untrendy, which could be attributed to odd circumstances. Moreover, the sensitivity of the differential rate terms was instrumental in discovering the overwhelming effect of independent indices on the dependent index. Clearly, this paper has accomplished interpolative and extrapolative prediction of energy indices and equally recommends for further investigation of the untrendy nature of oil price.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Nnamchi S N, Nnamchi A O, Onuorah M O, et al. Modelling and simulation of heat transfer through the finned hollow cylindrical surfaces of an insulation testing rig. World Journal of Modelling Simulation, 2019, 15(3): 243–261

    Google Scholar 

  2. Bianco V, Cascetta F, Marino A, et al. Understanding energy consumption and carbon emissions in Europe: a focus on inequality issues. Energy, 2019, 170: 120–130

    Article  Google Scholar 

  3. van den Brom P, Meijer A, Visscher H. Performance gaps in energy consumption: household groups and building characteristics. Building Research and Information, 2018, 46(1): 54–70

    Article  Google Scholar 

  4. Marion G, Lawson D. An introduction to mathematical modelling. Edinburgh: Bioinformatics Statistics Scotland, University of Bristol, 2008

  5. Afgan N H, Carvalho M G, Hovanov N V. Modeling of energy system sustainability index. Thermal Science, 2005, 9(2): 3–16

    Article  Google Scholar 

  6. Bianco V. Analysis of electricity consumption in the tourism sector: a decomposition approach. Journal of Cleaner Production, 2020, 248: 119286

    Article  Google Scholar 

  7. Li M, Mi Z, Coffman D D, et al. Assessing the policy impacts on non-ferrous metals industry’s CO2 reduction: evidence from China. Journal of Cleaner Production, 2018, 192: 252–261

    Article  Google Scholar 

  8. Sun M, Wang Y, Shi L, et al. Uncovering energy use, carbon emissions and environmental burdens of pulp and paper industry: a systematic review and meta-analysis. Renewable & Sustainable Energy Reviews, 2018, 92: 823–833

    Article  Google Scholar 

  9. Jha V, Karn S K, Kumar K. Review on the role of mathematical modeling in energy sectors. International Journal of Engineering Science Invention, 2017, 6 (12): 01–18

    Google Scholar 

  10. Oko C. Mathematical Modelling and Operations Research. Port Harcourt: University of Port Harcourt Press, 2009

    Google Scholar 

  11. Ukoba O K. Mathematical modeling: a tool for material corrosion prediction. Journal of Science and Technology, 2013, 3(4): 430–436

    Google Scholar 

  12. Wang J W, Liao H, Tang B J, et al. Is the CO2 emissions reduction from scale change, structural change or technology change? Evidence from non-metallic sector of 11 major economies in 1995–2009. Journal of Cleaner Production, 2017, 148: 148–157

    Article  Google Scholar 

  13. Hellel E K, Hamaci S, Ziani R. Modelling and reliability analysis of multi-source renewable energy systems using deterministic and stochastic Petri net. Open Automation and Control Systems Journal, 2018, 10(1): 25–40

    Article  Google Scholar 

  14. Zeng S, Nan X, Liu C, et al. The response of the Beijing carbon emissions allowance price (BJC) to macroeconomic and energy price indices. Energy Policy, 2017, 106(C): 111–121

    Article  Google Scholar 

  15. Kahn R, Whited T M. Identification is not causality, and vice versa. Review of Corporate Finance Studies, 2018, 7(1): 1–21

    Article  Google Scholar 

  16. Hejduková P, Kureková L. Causality as a ool for empirical analysis in economics. In: Proceedings of 4th Business & Management Conference. Prague, Czech Republic, 2016, 73–82

  17. Hossain Z, Rahman A, Hossain M, et al. Over-differencing and forecasting with non-stationary time series data. Dhaka University Journal of Science, 2019, 67(1): 21–26

    Article  Google Scholar 

  18. Cochrane J H. A brief parable of over-differencing. 2019-08-10, available at the website of johnhcochrane.blogspot.com

  19. Papana A, Kyrtsou C, Kugiumtzis D, et al. Detecting causality in non-stationary time series using partial symbolic transfer entropy: evidence in financial data. Computational Economics, 2016, 47(3): 341–365

    Article  Google Scholar 

  20. Cheng C, Sa-Ngasoongsong A, Beyca O, et al. Time series forecasting for nonlinear and non-stationary processes: a review and comparative study. IIE Transactions, 2015, 47(10): 1053–1071

    Article  Google Scholar 

  21. Turan T, Karakas M, Ozer H A. How do oil price changes affect the current account balance? Evidence from co-integration and causality tests. Ekonomicky Casopis, 2020, 68(1): 55–68

    Google Scholar 

  22. Mukhtaro S, Humbatova S I, Ingilab S, et al. The effect of financial development on energy consumption in the case of Kazakhstan. Journal of Applied Econometrics, 2020, 23(1): 75–88

    Google Scholar 

  23. Jian J, Fan X, He P, et al. The effects of energy consumption, economic growth and financial development on CO2 emissions in China: a VECM approach. Sustainability, 2019, 11(18): 4850

    Article  Google Scholar 

  24. Hechmy B. Testing for VECM Granger causality and cointegration between economic growth and renewable energy: evidence from MENA net energy importing countries. Econometric Research in Finance, 2019, 4(2): 111–131

    Google Scholar 

  25. Lv Z, Chu A M Y, McAleer M, et al. Modelling economic growth, carbon emissions, and fossil fuel consumption in China: cointegration and multivariate causality. International Journal of Environmental of Research, 2019, 16(21): 4176

    Google Scholar 

  26. Temiz Dinç D, Akdoğan E. Renewable energy production, energy consumption and sustainable economic growth in Turkey: a VECM approach. Sustainability, 2019, 11(5): 1273

    Article  Google Scholar 

  27. Rathnayaka R K T, Seneviratna D, Long W. The dynamic relationship between energy consumption and economic growth in China. Energy Sources. Part B, Economics, Planning, and Policy, 2018, 13(5): 264–268

    Article  Google Scholar 

  28. Zou X. VECM model analysis of carbon emissions, GDP, and international crude oil prices. Discrete Dynamics in Nature Society, 2018: 5350308

  29. Hasanov F, Bulut C, Suleymanov E. Review of energy-growth nexus: a panel analysis for ten Eurasian oil exporting countries. Renewable & Sustainable Energy Reviews, 2017, 73: 369–386

    Article  Google Scholar 

  30. Azlina A A. Energy consumption and economic development in Malaysia: a multivariate cointegration analysis. Procedia: Social and Behavioral Sciences, 2012, 65: 674–681

    Google Scholar 

  31. Sunde T. Energy consumption and economic growth modelling in SADC countries: an application of the VAR Granger causality analysis. International Journal of Energy Technology and Policy, 2020, 16(1): 41–56

    Article  MathSciNet  Google Scholar 

  32. Caruso G, Colantonio E, Gattone S A. Relationships between renewable energy consumption, social factors, and health: a panel vector auto regression analysis of a cluster of 12 EU countries. Sustainability, 2020, 12(7): 2915

    Article  Google Scholar 

  33. Zeng S, Liu Y, Ding J, et al. An empirical analysis of energy consumption, FDI and high quality development based on time series data of Zhejiang province. International Journal of Environmental Research and Public Health, 2020, 17(9): 3321

    Article  Google Scholar 

  34. Cui H, Zhu X, Wang H. Collaborative innovation of low-carbon technology from the triple helix perspective: exploring critical success factors based on DEMATEL-ISM. Polish Journal of Environmental Studies, 2020, 29(2): 1579–1592

    Article  Google Scholar 

  35. Nepal R, Paija N. A multivariate time series analysis of energy consumption, real output and pollutant emissions in a developing economy: new evidence from Nepal. Economic Modelling, 2019, 77: 164–173

    Article  Google Scholar 

  36. Fatima N, Li Y, Ahmad M, et al. Analyzing long-term empirical interactions between renewable energy generation, energy use, human capital, and economic performance in Pakistan. Energy, Sustainability and Society, 2019, 9(1): 42

    Article  Google Scholar 

  37. Liu Y, Roberts M C, Sioshansi R. A vector autoregression weather model for electricity supply and demand modeling. Journal of Modern Power Systems Clean Energy, 2018, 6(4): 763–776

    Article  Google Scholar 

  38. Rezitis A N, Ahammad S. The relationship between energy consumption and economic growth in south and southeast Asian countries: a panel VAR approach and causality analysis. International Journal of Energy Economics Policy, 2015, 5(3): 704–715

    Google Scholar 

  39. Antai A S, Udo A B, Ikpe I K. A VAR analysis of the relationship between energy consumption and economic growth in Nigeria. Journal of Economics Sustainable Development, 2015, 6(12): 1–12

    Google Scholar 

  40. Shu S, Chen J, Zhao X. Dynamic analysis of energy consumption and economic growth based on the PVAR model. Chemical Engineering Transactions, 2018, 67: 823–828

    Google Scholar 

  41. Tang Z, Shang J, Shi C, et al. Decoupling indicators of CO2 emissions from the tourism industry in China: 1990–2012. Ecological Indicators, 2019, 46(4): 390–397

    Google Scholar 

  42. Sun Y Y. Decomposition of tourism greenhouse gas emissions: revealing the dynamics between tourism economic growth, technological efficiency, and carbon emissions. Tourism Management, 2016, 55(C): 326–336

    Article  Google Scholar 

  43. Neusser K. Difference equations for economists preliminary and incomplete, 2019-09-05, available at the website of neusser.ch

  44. Tabaghdehi S A H. Market collusion and regime analysis in the US gasoline market. Journal of Economic Structures, 2018, 7(1): 9

    Article  Google Scholar 

  45. Frankel M S, Pyke C, Baumgartner S J, et al. Emerging strategies for effective energy efficiency benchmarking: normalization, economic analysis, and data aggregation. In: 2014 ACEEE Summer Study on Energy Efficiency in Buildings, 2014

  46. Adhikary S. Techniques and methods of business forecasting. 2019-09-10, available at the website of economicsdiscussion.net

  47. Watts R J, Porter A L. Innovation forecasting. Technological Forecasting and Social Change, 1997, 56(1): 25–47

    Article  Google Scholar 

  48. Adhikary S. Techniques and methods of business forecasting, 2019-09-08, available at the website of economicsdiscussion.net

  49. Birol F. The IEA’s WEO 2019 report. 2019-08-02, available at the website of iea.org

  50. Amadeo K. Oil price history—highs and lows since 1970: what makes oil prices so volatile? 2019-08-05, available at the website of thebalance.com

  51. Roser M. Human development index (HDI): empirical view. 2019-08-02, available at the website of ourworldindata.org

  52. McMahon T. Historical crude oil prices (Table) oil prices 1946-present. 2019-08-05, available at website of inflationdata

  53. Chair F. Crude oil prices-0 year historical chart, 2019-08-04, available at the website of macrotrends.net

  54. Wu B, Liu S, Zhu W, et al. Agricultural climatic regionalization for Longyan cultivation in Guangdong province. Journal of Tropical Meteorology, 2011, 27: 403–409

    Google Scholar 

  55. Lioudis N. What causes oil prices to fluctuate? 2020-06-12, available at the website of investopedia.com

Download references

Acknowledgements

Gratitude should be expressed to IEA and other energy bodies for the data used in this work.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Kampala International University, 20000, Kampala, Uganda

    Stephen Ndubuisi Nnamchi

  2. Department of Agricultural Engineering and Bio Resources, Michael Okpara University of Agriculture, Umudike, Umuahia, Nigeria

    Onyinyechi Adanma Nnamchi

  3. Directorate of Human Resource/Finance, Kampala International University, 20000, Kampala, Uganda

    Janice Desire Busingye

  4. Department of Mathematics and Statistics, Faculty of Sciences, University of Port Harcourt, PMB 5323 Choba Port, Harcourt, Nigeria

    Maxwell Azubuike Ijomah

  5. Department of Macroeconomic Analysis (Ministry of Finance), Budget and National Planning, Abuja, Nigeria

    Philip Ikechi Obasi

Authors
  1. Stephen Ndubuisi Nnamchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Onyinyechi Adanma Nnamchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Janice Desire Busingye
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maxwell Azubuike Ijomah
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Philip Ikechi Obasi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stephen Ndubuisi Nnamchi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nnamchi, S.N., Nnamchi, O.A., Busingye, J.D. et al. Modeling, simulation, and prediction of global energy indices: a differential approach. Front. Energy 16, 375–392 (2022). https://doi.org/10.1007/s11708-021-0723-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11708-021-0723-6

Keywords

Search

Navigation