Rural Electrification Peak Load Demand Forecast Model Based on End User Demographic Data

Anthony Mfonobong Umoren, Nseobong I. Okpura, Idorenyin Markson


In this paper a rural electrification peak load demand forecast model was developed based on the readily available United Nation and World Bank data on the electric power consumption in KWh per capita, along with the population and land mass data of the rural community. Furthermore, in the situation where there is no available data on the land mass area, a web map applications can be used for computing the land mass area in Km2 for the project coverage area. In this paper, the rural community used as the case study was Orji town in Owerri North local government area in Imo state, Nigeria. The forecast results showed that the part of Orji community that was considered in the study had landmass area of 2.4760387Km2; a population of 2898 in 2015 and peak load demand of 81.14 KVA in the same year with 45% population having access to electricity. However, in 2025, the same part of Orji town will have a population of 3971.791 and peak load demand of 261.79 KVA with 75% population having access to electricity.


Rural Electrification; Load factor; Load demand; Peak load Demand; Demography; Electric Demand Forecast; Electric Demand Per Capita

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Niez, A. (2010). Comparative study on rural electrification policies in emerging economies, International Energy Agency, France.

Hevia, T. (2009). The rural electrification in China and the impact of renewable energies. EU–China Business Management Training Project, Student Research Projects/outputs, 42.

Anandan, M. and Ramaswamy S. (2014) Rural Electrification: Pros And Cons, Strategies And Policies. In National Conference on "Renewable Energy Innovations for Rural Development" NCREIRD 2014 Organized by "Rural Energy Centre, Gandhigram Rural Institute -Deemed University, Tamilnadu".

Islam, A., Hasib, S. R., & Islam, M. S. (2013). Short term electricity demand forecasting of an isolated area using two different approaches. Journal of Power Technologies, 93(4), 185-193.

Ghods, L., & Kalantar, M. (2011). Different methods of long-term electric load demand forecasting; a comprehensive review. Iranian Journal of Electrical & Electronic Engineering, 7(4), 249-259.

Mamun M.A. and Nagasaka K. (2004) Artificial neural networks applied to long-term electricity demand forecasting, Proceedings of the Fourth International Conference on Hybrid Intelligent Systems (HIS'04), 204-209.

Aslan Y., Yavasca S., Yasar C. (2010). Long term Electric Peak load forecasting of Kutahya using different approaches. 6th International Conference on “Technical and Physical Problems of Power Engineering”, 191-195.

Badran, S. M., & Abouelatta, O.B. (2011). Forecasting Electrical Load using ANN Combined with Multiple Regression Method. The Research Bulletin of Jordan ACM, 2(2), 152-158.

Fahad, M. U., & Arbab, N. (2014). Factor Affecting Short Term Load Forecasting.Journal of Clean Energy Technologies, 2(4), 305-309.

Saroha, S. (2012). Forecasting issues in present day power systems, PhD Synopsis. MAHARISHI MARKANDESHWAR UNIVERSITY.

Almeshaiei, E., & Soltan, H. (2011). A methodology for electric power load forecasting. Alexandria Engineering Journal, 50(2), 137-144.

Hirst, E. (2003). Long-term resource adequacy: The role of demand resources. Consulting in Electric-Industry Restructuring, EstadosUnidos.

Hirst, E., Schweitzer, M., Yourstone, E., & Eto, J. (1991). Technical competence of integrated resource plans prepared by electric utilities. Resources and Energy, 13(1), 39-55.

Meier, P. (1990). Power sector innovation in developing countries: Implementing investment planning under capital and environmental constraints. Annual Review of Energy, 15(1), 277-306.

Adam, M. E. A. (2012). Electrical Energy Demand Forecasting in Nyala city 2012-2025 (Doctoral dissertation, Sudan University of Science & Technology).

Schellong, W. (2011). Energy demand analysis and forecast, Energy Management Systems, 5, Dr Giridhar Kini (Ed.), INTECH Open Access Publisher, 101-120.

Al-Hamadi, H. M., & Soliman, S. A. (2005). Long-term/mid-term electric load forecasting based on short-term correlation and annual growth. Electric Power Systems Research, 74(3), 353-361.

Ye, S., Zhu, G., Xiao, Z. (2012). Long term load forecasting and recommendations for China based on support vector regression, Energy and Power Engineering, 4, 380-385.

World Development Indicators (2015). Electric power consumption (kWh per capita)


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