Modeling Electricity Generation and Consumption in Cameroon

Abstract

Currently, there is a significant gap between electricity generation and consumption in Cameroon. Research has shown that electricity consumption in the country is estimated to increase by 965.7 GWh in five years, from 2020 to 2024 due to demographic and economic growth. Hence, this study aims to find methods that can be useful in developing strategies to balance the energy supply and demand in the country. This is done by developing models that can predict future electrical power consumption and generation. Correlation analysis and regression analysis were performed by using data obtained from various databases, and related models were developed accordingly. The model parameters were carbon dioxide emissions, electricity consumption per capita, final consumption expenditures, electricity installed capacity, fossil fuel installed capacity, labor force, and GDP. The models' results demonstrated excellent performance coefficients with RMSE of 0.17041, 0.23893, 0.27571, and 0.2465 for hydroelectricity generation, fossil fuel electricity generation, net electricity generation, and net electricity consumption respectively. Also, hydroelectricity generation, net electricity generation, and net electricity consumption models showed very good RRMSE performance indicating that the models can make predictions with only 4.26%, 5.26%, and 5.77% deviation from the mean values of hydroelectricity generation, net electricity generation, and net electricity consumption, respectively.

Keywords

• Cameroon
• Correlation Analysis
• Electricity Consumption
• Electricity Generation
• Regression Analysis

References

1. Guefano, S., Tamba, J. G., Azong, T. E. W., & Monkam, L. (2021). Forecast of electricity consumption in the Cameroonian residential sector by Grey and vector autoregressive models. Energy, 214.
2. Guefano, S., Tamba, J. G., Monkam, L., & Bonoma, B. (2020). Forecast for the Cameroon’s residential electricity demand based on the multilinear regression model. Energy and Power Engineering, 12(05).
3. Dieudonné, N. T., Armel, T. K. F., Vidal, A. K. C., & René, T. (2022). Prediction of electrical energy consumption in Cameroon through econometric models. Electric Power Systems Research, 210.
4. African Development Bank Group. (2021). Country priority plan and diagnostic of the electricity sector Cameroon 3.
5. Kidmo, D. K., Deli, K., & Bogno, B. (2021). Status of renewable energy in Cameroon. Renewable Energy and Environmental Sustainability, 6, 2.
6. World Bank Database. (2022).
7. Tamba, J. G., Nsouandélé, J. L., Fopah Lélé, A., & Sapnken, F. E. (2017). Electricity consumption and economic growth: Evidence from Cameroon. Energy Sources, Part B: Economics, Planning and Policy, 12(11), 1007–1014.
8. Joel, S., & Nguepdjio Cyrille, N. (2019). Consommation d’énergie, croissance économique et émissions de Co2 au Cameroun: une analyse de causalité. African Integration and Development Review.

9. Ibeh, L., & Agwu, C. (2011). Comparison of regression model and artificial neural network model for the prediction of electrical power generated in Nigeria. Spatial Dynamics of Urban Poverty and Environment Nexus.
10. El, I., Bannari, R., Abouabdellah, A., & Guerrero, J. M. (2017). Energy production: A comparison of forecasting methods using the polynomial curve fitting and linear regression. In 2017 International Renewable and Sustainable Energy Conference (IRSEC).
11. Verma, T., Tiwana, A. P. S., Reddy, C. C., Arora, V., & Devanand, P. (2016). Data analysis to generate models based on neural network and regression for solar power generation forecasting. Proceedings - International Conference on Intelligent Systems, Modelling and Simulation (ISMS), 97–100.
12. Uyanık, G. K., & Güler, N. (2013). A study on multiple linear regression analysis. Procedia - Social and Behavioral Sciences, 106, 234–240.
13. U.S. Energy Information Administration Database. (2022).
14. Worldometers. (2022). Cameroon natural gas.
15. Mukaka, M. M. (2012). Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi Medical Journal, 24(3).
16. Despotovic, M., Nedic, V., Despotovic, D., & Cvetanovic, S. (2015). Review and statistical analysis of different global solar radiation sunshine models. Renewable and Sustainable Energy Reviews, 52, 1869–1880.
17. Liu, R. X., Kuang, J., Gong, Q., & Hou, X. L. (2003). Principal component regression analysis with SPSS. Computer Methods and Programs in Biomedicine, 71(2), 141–147.
18. Fombuwing, B. (2023). Modeling electricity generation and consumption in Cameroon. MSc Thesis, Cyprus International University.
19. Comert, M., & Yildiz, A. (2022). A novel artificial neural network model for forecasting electricity demand enhanced with population-weighted temperature mean and the unemployment rate. Turkish Journal of Engineering, 6(2), 178-189.
20. Ayyildiz, E., & Murat, M. (2024). A lasso regression-based forecasting model for daily gasoline consumption: Türkiye case. Turkish Journal of Engineering, 8(1), 162-174.
21. Yadav, A. K., & Chandel, S. S. (2014). Solar radiation prediction using artificial neural network techniques: A review. Renewable and Sustainable Energy Reviews, 33, 772–781.
22. Tatietse, T. T., Kemajou, A., & Diboma, S. (2010). Electricity self-generation costs for industrial companies in Cameroon. Energies, 3(7).
23. USAID. (2016). Cameroon Power Africa fact sheet.
24. MINEPDED. (n.d.). Cameroon climate investment fund, Ministry of Environment and protection of nature. Sustainable Development, 1-10.
25. Wirba, A. V., Mas'ud, A., Abdullahi, A., & Firdaus, M. (2015). Renewable energy potentials in Cameroon: Prospects and challenges. Renew Energy.