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Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model

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  • Xinyu Han

    (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)

Abstract

Forecasting energy demand is the basis for sustainable energy development. In recent years, the new discovery of East Africa’s energy has completely reversed the energy shortage, having turned the attention of the world to the East African region. Systematic research on energy forecasting in Africa, particularly in East Africa, is still relatively rare. In view of this, this study uses a variety of methods to comprehensively predict energy consumption in East Africa. Based on the traditional grey model, this study: (1) Integrated the power coefficient and metabolic principles, and then proposed non-linear metabolic grey model (NMGM) forecasting model; (2) Used Auto Regressive Integrated Moving Average Model (ARIMA) for secondary modeling, and then developed a metabolic grey model-Auto Regressive Integrated Moving Average Model (MGM-ARIMA) and non-linear metabolic grey model-Auto Regressive Integrated Moving Average Model (NMGM-ARIMA) combined models. In terms of the prediction interval, the data for 2000–2017 is a fit to the past stage, while the data for 2018–2030 is used for the prediction of the future stage. To measure the effect of the prediction, the study used the average relative error indicator to evaluate the accuracy of different models. The results indicate that: (1) Mean relative errors of NMGM, MGM-ARIMA, and NMGM-ARIMA are 2.9697%, 2.0969%, and 1.4654%, proving that each prediction model is accurate; (2) Compared with the single model, the combined model has higher precision, confirming the superiority and feasibility of model combination. After prediction, the conclusion shows that East Africa’s primary energy consumption will grow by about 4 percent between 2018 and 2030. In addition, the limitation of this study is that only single variable are considered.

Suggested Citation

  • Xinyu Han & Rongrong Li, 2019. "Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model," Energies, MDPI, vol. 12(17), pages 1-24, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3278-:d:261069
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    as
    1. Tsikalakis, Antonis & Tomtsi, T. & Hatziargyriou, N.D. & Poullikkas, A. & Malamatenios, Ch. & Giakoumelos, E. & Jaouad, O. Cherkaoui & Chenak, A. & Fayek, A. & Matar, T. & Yasin, A., 2011. "Review of best practices of solar electricity resources applications in selected Middle East and North Africa (MENA) countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 2838-2849, August.
    2. Wang, Qiang & Song, Xiaoxin, 2019. "Forecasting China's oil consumption: A comparison of novel nonlinear-dynamic grey model (GM), linear GM, nonlinear GM and metabolism GM," Energy, Elsevier, vol. 183(C), pages 160-171.
    3. Ouedraogo, Nadia S., 2017. "Modeling sustainable long-term electricity supply-demand in Africa," Applied Energy, Elsevier, vol. 190(C), pages 1047-1067.
    4. Inglesi-Lotz, R., 2011. "The evolution of price elasticity of electricity demand in South Africa: A Kalman filter application," Energy Policy, Elsevier, vol. 39(6), pages 3690-3696, June.
    5. Al-mulali, Usama & Binti Che Sab, Che Normee, 2012. "The impact of energy consumption and CO2 emission on the economic growth and financial development in the Sub Saharan African countries," Energy, Elsevier, vol. 39(1), pages 180-186.
    6. Komendantova, Nadejda & Patt, Anthony & Barras, Lucile & Battaglini, Antonella, 2012. "Perception of risks in renewable energy projects: The case of concentrated solar power in North Africa," Energy Policy, Elsevier, vol. 40(C), pages 103-109.
    7. Esso, Loesse Jacques, 2010. "Threshold cointegration and causality relationship between energy use and growth in seven African countries," Energy Economics, Elsevier, vol. 32(6), pages 1383-1391, November.
    8. Tigabu, Aschalew D. & Berkhout, Frans & van Beukering, Pieter, 2015. "Technology innovation systems and technology diffusion: Adoption of bio-digestion in an emerging innovation system in Rwanda," Technological Forecasting and Social Change, Elsevier, vol. 90(PA), pages 318-330.
    9. Ziramba, Emmanuel, 2010. "Price and income elasticities of crude oil import demand in South Africa: A cointegration analysis," Energy Policy, Elsevier, vol. 38(12), pages 7844-7849, December.
    10. Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
    11. Ding, Song & Hipel, Keith W. & Dang, Yao-guo, 2018. "Forecasting China's electricity consumption using a new grey prediction model," Energy, Elsevier, vol. 149(C), pages 314-328.
    12. Sanoh, Aly & Kocaman, Ayse Selin & Kocal, Selcuk & Sherpa, Shaky & Modi, Vijay, 2014. "The economics of clean energy resource development and grid interconnection in Africa," Renewable Energy, Elsevier, vol. 62(C), pages 598-609.
    13. Lee, N.C. & Leal, V.M.S., 2014. "A review of energy planning practices of members of the Economic Community of West African States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 202-220.
    14. Gaojun Zhang & Jinfeng Wu & Bing Pan & Junyi Li & Minjie Ma & Muzi Zhang & Jian Wang, 2017. "Improving daily occupancy forecasting accuracy for hotels based on EEMD-ARIMA model," Tourism Economics, , vol. 23(7), pages 1496-1514, November.
    15. Mentis, Dimitrios & Hermann, Sebastian & Howells, Mark & Welsch, Manuel & Siyal, Shahid Hussain, 2015. "Assessing the technical wind energy potential in Africa a GIS-based approach," Renewable Energy, Elsevier, vol. 83(C), pages 110-125.
    16. Chen, Chun-I, 2008. "Application of the novel nonlinear grey Bernoulli model for forecasting unemployment rate," Chaos, Solitons & Fractals, Elsevier, vol. 37(1), pages 278-287.
    17. Schwerhoff, Gregor & Sy, Mouhamadou, 2017. "Financing renewable energy in Africa – Key challenge of the sustainable development goals," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 393-401.
    18. Aasim, & Singh, S.N. & Mohapatra, Abheejeet, 2019. "Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting," Renewable Energy, Elsevier, vol. 136(C), pages 758-768.
    19. Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
    20. Odhiambo, Nicholas M., 2009. "Electricity consumption and economic growth in South Africa: A trivariate causality test," Energy Economics, Elsevier, vol. 31(5), pages 635-640, September.
    21. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "China's dependency on foreign oil will exceed 80% by 2030: Developing a novel NMGM-ARIMA to forecast China's foreign oil dependence from two dimensions," Energy, Elsevier, vol. 163(C), pages 151-167.
    22. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques," Energy, Elsevier, vol. 161(C), pages 821-831.
    23. Rafindadi, Abdulkadir Abdulrashid, 2016. "Does the need for economic growth influence energy consumption and CO2 emissions in Nigeria? Evidence from the innovation accounting test," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 1209-1225.
    24. Md Zakir Hossain & Quazi Abdus Samad & Md Zulficar Ali, 2006. "ARIMA model and forecasting with three types of pulse prices in Bangladesh: a case study," International Journal of Social Economics, Emerald Group Publishing, vol. 33(4), pages 344-353, April.
    25. Nadia S. Ouedraogo, 2017. "Modeling sustainable long-term electricity supply-demand in Africa," WIDER Working Paper Series wp-2017-23, World Institute for Development Economic Research (UNU-WIDER).
    26. Ludlow, Jorge & Enders, Walter, 2000. "Estimating non-linear ARMA models using Fourier coefficients," International Journal of Forecasting, Elsevier, vol. 16(3), pages 333-347.
    27. Madubansi, M. & Shackleton, C.M., 2006. "Changing energy profiles and consumption patterns following electrification in five rural villages, South Africa," Energy Policy, Elsevier, vol. 34(18), pages 4081-4092, December.
    28. Nadia S. Ouedraogo, 2017. "Modeling sustainable long-term electricity supply–demand in Africa," WIDER Working Paper Series 023, World Institute for Development Economic Research (UNU-WIDER).
    29. Lacher, Wolfram & Kumetat, Dennis, 2011. "The security of energy infrastructure and supply in North Africa: Hydrocarbons and renewable energies in comparative perspective," Energy Policy, Elsevier, vol. 39(8), pages 4466-4478, August.
    30. Gnansounou, Edgard & Bayem, Herman & Bednyagin, Denis & Dong, Jun, 2007. "Strategies for regional integration of electricity supply in West Africa," Energy Policy, Elsevier, vol. 35(8), pages 4142-4153, August.
    31. Declan Conway & Carole Dalin & Willem A. Landman & Timothy J. Osborn, 2017. "Hydropower plans in eastern and southern Africa increase risk of concurrent climate-related electricity supply disruption," Nature Energy, Nature, vol. 2(12), pages 946-953, December.
    32. Wang, Qiang & Li, Shuyu & Li, Rongrong & Ma, Minglu, 2018. "Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model," Energy, Elsevier, vol. 160(C), pages 378-387.
    33. Lebotsa, Moshoko Emily & Sigauke, Caston & Bere, Alphonce & Fildes, Robert & Boylan, John E., 2018. "Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem," Applied Energy, Elsevier, vol. 222(C), pages 104-118.
    34. Shiyi Chen & Kiho Jeong & Wolfgang Härdle, 2015. "Recurrent support vector regression for a non-linear ARMA model with applications to forecasting financial returns," Computational Statistics, Springer, vol. 30(3), pages 821-843, September.
    35. Emodi, Nnaemeka Vincent & Emodi, Chinenye Comfort & Murthy, Girish Panchakshara & Emodi, Adaeze Saratu Augusta, 2017. "Energy policy for low carbon development in Nigeria: A LEAP model application," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 247-261.
    36. Xu, Ning & Ding, Song & Gong, Yande & Bai, Ju, 2019. "Forecasting Chinese greenhouse gas emissions from energy consumption using a novel grey rolling model," Energy, Elsevier, vol. 175(C), pages 218-227.
    37. Truong, D.Q. & Ahn, K.K., 2012. "Wave prediction based on a modified grey model MGM(1,1) for real-time control of wave energy converters in irregular waves," Renewable Energy, Elsevier, vol. 43(C), pages 242-255.
    38. Wang, Qiang & Song, Xiaoxing & Li, Rongrong, 2018. "A novel hybridization of nonlinear grey model and linear ARIMA residual correction for forecasting U.S. shale oil production," Energy, Elsevier, vol. 165(PB), pages 1320-1331.
    39. Matyjaszek, Marta & Riesgo Fernández, Pedro & Krzemień, Alicja & Wodarski, Krzysztof & Fidalgo Valverde, Gregorio, 2019. "Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory," Resources Policy, Elsevier, vol. 61(C), pages 283-292.
    40. Bahrami, Saadat & Hooshmand, Rahmat-Allah & Parastegari, Moein, 2014. "Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm," Energy, Elsevier, vol. 72(C), pages 434-442.
    41. Fant, Charles & Adam Schlosser, C. & Strzepek, Kenneth, 2016. "The impact of climate change on wind and solar resources in southern Africa," Applied Energy, Elsevier, vol. 161(C), pages 556-564.
    42. Anca Mehedintu & Mihaela Sterpu & Georgeta Soava, 2018. "Estimation and Forecasts for the Share of Renewable Energy Consumption in Final Energy Consumption by 2020 in the European Union," Sustainability, MDPI, vol. 10(5), pages 1-22, May.
    43. Tigabu, Aschalew Demeke & Berkhout, Frans & van Beukering, Pieter, 2015. "The diffusion of a renewable energy technology and innovation system functioning: Comparing bio-digestion in Kenya and Rwanda," Technological Forecasting and Social Change, Elsevier, vol. 90(PA), pages 331-345.
    44. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
    45. Mulugetta, Yacob, 2009. "Evaluating the economics of biodiesel in Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1592-1598, August.
    46. Kivyiro, Pendo & Arminen, Heli, 2014. "Carbon dioxide emissions, energy consumption, economic growth, and foreign direct investment: Causality analysis for Sub-Saharan Africa," Energy, Elsevier, vol. 74(C), pages 595-606.
    47. Barry, Marie-Louise & Steyn, Herman & Brent, Alan, 2011. "Selection of renewable energy technologies for Africa: Eight case studies in Rwanda, Tanzania and Malawi," Renewable Energy, Elsevier, vol. 36(11), pages 2845-2852.
    48. Sokona, Youba & Mulugetta, Yacob & Gujba, Haruna, 2012. "Widening energy access in Africa: Towards energy transition," Energy Policy, Elsevier, vol. 47(S1), pages 3-10.
    49. Wu, Lifeng & Liu, Sifeng & Liu, Dinglin & Fang, Zhigeng & Xu, Haiyan, 2015. "Modelling and forecasting CO2 emissions in the BRICS (Brazil, Russia, India, China, and South Africa) countries using a novel multi-variable grey model," Energy, Elsevier, vol. 79(C), pages 489-495.
    50. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2019. "Will Trump's coal revival plan work? - Comparison of results based on the optimal combined forecasting technique and an extended IPAT forecasting technique," Energy, Elsevier, vol. 169(C), pages 762-775.
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