2030 Energy Pathways in Côte d’Ivoire: “A Business as Usual” Analysis


  • Yessoh Gaudens Thecle Edjoukou School of Management, Jinan University, Guangzhou 510632, China
  • Bangzhu Zhu Business School, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • Minxing Jiang Business School, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • Akadje Jean Roland Edjoukou School of Accounting, Dongbei University of Finance and Economics, Dalian, China,




primary energy demand, energy forecast, energy, ARIMA, forecasting, Côte d’Ivoire


Forecasting future energy demand values is of paramount importance for proper resource planning. This paper examines energy outlook for the coming decade in Côte d’Ivoire presented as a business as usual scenario. We, therefore, build a forecasting model using the Autoregressive Integrated Moving Average (ARIMA) to estimate primary energy demand and energy demand by fuels. The results indicate that energy demand will increase steadily within the forecasted period (2017-2030). However, the annual growth rate of each fuel,, including the primary energy demand item, will first rise from the year 1990 to the year 2016 and then decrease within the forecasted period except hydropower that will experience a steady increase from 1990 to 2030. Furthermore, it is noticed that the energy structure of the country will still be biofuels (fuelwood and charcoal) intensive with a significant presence of conventional sources of energy. Based on these findings, we propose some policy recommendations.


United Nations, Sustainable Energy for all (SE4all), A Vision Statement by Ban Ki-moon Secretary-General of the United Nations. 2011.


World Bank, Cote d'Ivoire. 2019.


Wadjamsse Baudelaire Djezou, ANALYSE DES DETERMINANTS DE L’EFFICACITE ENERGETIQUE DANS L’ESPACE UEMOA. European Scientific Journal 2013. 9(12).

Chang, P.-C., C.-Y. Fan and J.-J. Lin, Monthly electricity demand forecasting based on a weighted evolving fuzzy neural network approach. International Journal of Electrical Power Energy Systems, 2011. 33(1): p. 17-27.

Kermanshahi, B. and H. Iwamiya, Up to the year 2020 load forecasting using neural nets. International Journal of Electrical Power Energy Systems, 2002. 24(9): p. 789-797.

G.Tamizharasi, S.K., K.S.Sreenivasan, Energy Forecasting using Artificial Neural Networks. International Journal of Advanced Research in Electrical, Electronics, and Instrumentation Engineering, 2014. 3(3).

Hamedmoghadam, H., N. Joorabloo, and M. Jalili, Australia's long-term electricity demand forecasting using deep neural networks. ArXiv, 2018. abs/1801.02148.

Feilat, E.A., and M. Bouzguenda. Medium-term load forecasting using neural network approach. in 2011 {IEEE} {PES} Conference on Innovative Smart Grid Technologies - Middle East. 2011. {IEEE}.

T.Q.D.Khoa et al. Application of wavelet and neural network to long-term load forecasting. in 2004 International Conference on Power System Technology, 2004. {PowerCon} 2004. 2004. {IEEE}.

Shijie Ye et al., Long Term Load Forecasting and Recommendations for China Based on Support Vector Regression. Energy and Power Engineering, 2012. 04(05): p. 380-385.

Kucukdeniz, T., long-term electricity demand forecasting: an alternative approach with support vector machine. Istanbul University of Engineering Sciences, 2010. 1: p. 45-53.

M. Çunkaş and Altun, A.A., Long Term Electricity Demand Forecasting in Turkey Using Artificial Neural Networks. Energy Sources, Part B: Economics, Planning, and Policy, 2010. 5(3): p. 279-289.

Mati, A.A., et al., Electricity Demand Forecasting in Nigeria using Time Series Model.

Albayrak, A.S., ARIMA Forecasting of Primary Energy Production and Consumption in Turkey: 1923–2006. Enerji, Piyasa ve Düzenleme, 2010. 1(1): p. 24-50.

Jiang, F., X. Yang, and S. Li, Comparison of Forecasting India's Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model. Sustainability, 2018. 10(7): p. 2225.

Miao, J. The Energy Consumption Forecasting in China Based on ARIMA Model. in Proceedings of the 2015 International Conference on Materials Engineering and Information Technology Applications. 2015. Atlantis Press.

Rehman, S., et al., An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan. Energies, 2017. 10(11): p. 1868.

Ediger, V.c. and S.c. Akar, ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy, 2007. 35(3): p. 1701-1708.

Ghalehkhondabi, I., et al., An overview of energy demand forecasting methods published in 2005-2015. Energy Systems, 2016. 8(2): p. 411-447.

Hansen, J.V., and R.D. Nelson, Time-series analysis with neural networks and ARIMA-neural network hybrids. Journal of Experimental Theoretical Artificial Intelligence, 2003. 15(3): p. 315-330.

Sallehuddin, R., S.M. Shamsuddin, and S.Z.M. Hashim. Hybridization Model of Linear and Nonlinear Time Series Data for Forecasting. in 2008 Second Asia International Conference on Modelling Simulation ({AMS}). 2008. {IEEE}.

Valenzuela, O., et al., Hybridization of intelligent techniques and ARIMA models for time series prediction. Fuzzy Sets and Systems, 2008. 159(7): p. 821-845.

Wedding, D.K. and K.J. Cios, Time series forecasting by combining RBF networks, certainty factors, and the Box-Jenkins model. Neurocomputing, 1996. 10(2): p. 149-168.

Zeng, D., et al. Short Term Traffic Flow Prediction Using Hybrid ARIMA and ANN Models. in 2008 Workshop on Power Electronics and Intelligent Transportation System. 2008. {IEEE}.

Zhang, G.P., Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 2003. 50: p. 159-175.

Che, J. and J. Wang, Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling. Energy Conversion and Management, 2010. 51(10): p. 1911-1917.

Lo, J.-H. A study of applying ARIMA and SVM model to software reliability prediction. in 2011 International Conference on Uncertainty Reasoning and Knowledge Engineering. 2011. {IEEE}.

Xiang, L., G.-j. Tang, and C. Zhang. Simulation of Time Series Prediction Based on Hybrid Support Vector Regression. in 2008 Fourth International Conference on Natural Computation. 2008. {IEEE}.

Bao, Y., et al., A Comparative Study on Hybrid Linear and Nonlinear Modeling Framework for Air Passenger Traffic Forecasting. {INTERNATIONAL} {JOURNAL} {ON} Advances in Information Sciences and Service Sciences, 2011. 3(5): p. 243-254.

Khashei, M., A.Z. Hamadani, and M. Bijari, A novel hybrid classification model of artificial neural networks and multiple linear regression models. Expert Systems with Applications, 2012. 39(3): p. 2606-2620.

Lai, K.K., et al., Hybridizing Exponential Smoothing and Neural Network for Financial Time Series Predication, in Computational Science {ICCS} 2006. 2006, Springer Berlin Heidelberg. p. 493-500.

Alwee, R., S.M.H. Shamsuddin, and R. Sallehuddin, Hybrid Support Vector Regression and Autoregressive Integrated Moving Average Models Improved by Particle Swarm Optimization for Property Crime Rates Forecasting with Economic Indicators. The Scientific World Journal, 2013. 2013: p. 1-11.

Esso Jacques, The Energy Consumption-Growth Nexus in Seven Sub-Saharan African Countries. Economics Bulletin, 2010. 30(2): p. 1191-1209.

Kouakou, A.K., Economic growth and electricity consumption in Cote d}Ivoire: Evidence from time series analysis. Energy Policy, 2011. 39(6): p. 3638-3644.

Box, G.E.P.a.J., G.M., Time Series Analysis: Forecasting and Control, ed. Robinson E. 1976, Holden-Day: San Francisco, CA, USA.

Chatfield, C., Model uncertainty, and forecast accuracy. Journal of Forecasting, 1996. 15(7): p. 495-508.

(IEA), I.E.A., Statistics: Cote d'Ivoire. 2018.

International Energy Agency, Energy Balances of Non-OECD Countries/ World Energy Balances. 2018 ed. 2019: IEA.

Fells Ian, Fossil fuels 1850 to 2000, in Energy World. 1991, Institute of Energy. p. 13-16.


Kassi, D., A. Nasiri, and A.J.R. Edjoukou, Financial Development, Economic Growth, and Energy Consumption Nexus in Cote d'Ivoire. International Journal of Finance Banking Studies (2147-4486), 2017. 6(3): p. 1.

SNEDAI. Comprendre la centrale thermique à "charbon propre" de San-Pédro. 2019; Available from: https://snedai.com/comprendre-la-centrale-thermique-a-charbon-propre-de-san-pedro/.

Aboh Prisca Zidago and Wu Zhangqi, Analysis of Fuelwood and Charcoal Sector in Cote d'Ivoire. International Journal of Science and Technology. 4 (1), 2015. 4(1): p. 9-13.

Diesendorf, M., Models of sustainability and sustainable development. International Journal of Agricultural Resources, Governance, and Ecology, 2001. 1(2): p. 109.

Autorité Nationale de Régulation du Secteur de l'électricité, Rapport d'activités 2017. 2018.


Koua, B.K., et al., Present status and overview of potential of renewable energy in Cote d'Ivoire. Renewable and Sustainable Energy Reviews, 2015. 41: p. 907-914.



How to Cite

Edjoukou, Y. G. T., Zhu, B., Jiang, M., & Edjoukou, A. J. R. (2019). 2030 Energy Pathways in Côte d’Ivoire: “A Business as Usual” Analysis. Asian Journal of Applied Sciences, 7(6). https://doi.org/10.24203/ajas.v7i6.6031

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