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Electrical Energy Demand Forecast in Nigeria Between 2020 - 2040 Using Probabilistic Extrapolation Method

Year 2021, Volume: 5 Issue: 3, 71 - 85, 30.09.2021

Abstract

Precise load forecasting is very vital for electrical energy utilities in a deregulated electricity market. Reliable and sufficient access to the electric power needed by several homes and businesses remain a great obstacles facing Nigeria. This paper focused on Nigeria electricity demand forecast from 2020 – 2040 using time series analysis on past load demand. The issue of Nigeria electricity supply challenges and possible solution or way forward for sufficient power has been discussed. Several load forecasting techniques, classification over the last few decades and review of previous work on this subject are also presented in this work. On the basics of these review the stochastic/probabilistic extrapolation method were employed. MATLAB was used for the computation and the results were analyzed and discussed. It was observed that there is a great positive link between the electricity demand and the years that as the year advances the demand for a reliable and affordable electrical energy supply increases. From the total predicted load demand, it is seen that Nigeria need over 17,000 MW in 2021 and over 23,000 MW in 2040 to be able to cater for the growing need of Nigerians. The average mean square error which determines the accuracy or precision of forecast was found to be approximately 0.52%. Load forecasting is needed to coordinate transmission and distribution outages over the network and reduce failure rate in the network. Load forecast are extremely important for energy suppliers, financial institutions and other users in electric energy generation, transmission, distribution and market.

Supporting Institution

University of Port Harcourt, Rivers State, Nigeria

Project Number

936845

Thanks

Many thanks to editors for their time and patience in reviewing this work

References

  • [1] U. P. Onochie, H. O. Egware, and T. O. Eyakwanor, The Nigeria Electric Power Sector, Opportunities and Challenges. Journal of Multidisciplinary Engineering Science and Technology (JMEST). Vol. 2 Issue 4, April – 2015.
  • [2] A. I. Obanor, An Intergrated and Pragmatic Approach to the Power Challenges in Nigeria. A lecture delivered at the 1st Public Lecture/Award Ceremony organized by Nigeria Institute of Mechanical Engineers (NIMechE) in Benin City, Edo State Nigeria. 2015.
  • [3] F. Adesola, A lecture on Data Capture Processing, 2006 Population & Housing Census of Nigeria, Dar-es-salaam, Tanzania. 2008.
  • [4] The Vanguard Nigeria Newspaper, February 1, 2019: http://www.vanguardngr.com/2019
  • [5] Worldometers, Department of economics and social affairs, population division. World population prospects, the 2019 Revision www.worldometers.info//
  • [6] UN Report, World population prospect: The 2017 Revision, www.vanguardngr.com/2017/06/nigeria-become-3rd-populous-country-2050-un-report/
  • [7] O. Obafemi et al (2018) IOP Conf. Ser.: Mater. Sci. Eng. 413 012053
  • [8] Nigerian Electricity Regulatory Commission. https://nerc.gov.ng
  • [9] N. Mellersh (2015), A Scramble for Power- The Nigeria Energy Crisis. African Law and Business” http://www.africanlawbusiness.com/news/5679 accessed on 27/05/2016
  • [10] The Nation Nigeria Newspaper May 10, 2019, https://thenationonlineng.net/2019/
  • [11] The Federal Ministry of Power Information Bureau – Abuja Nigeria, http://www.power.gov.ng/
  • [12] K. S. Arunesh, I. Khatoon, S. Muazzam, and D. K. Chaturvedi, Load Forecasting Techniques and Methodologies: A Review, International Conference on Power, Control and embedded systems. 2012, 631- 636
  • [13] G. Gross, and F. D. Galiana, Short Term Load Forecasting. Proceedings of the IEEE, Vol. 75, 1987, 1558-1573
  • [14] Q. Ding, Long-Term Load Forecast Using Decision Tree Method, Power Systems Conference and Exposition, PSCE 06, IEEE PES, Vol.1, 2006,1541-1543
  • [15] G. A. N. Mbamalu, and M. E. El-Hawary, Load Forecasting Via Suboptimal Seasonal Autoregressive Models and Iteratively Reweighted Least Squares Estimation. IEEE Transaction on Power System, 8, 1992,.343-348
  • [16] S. Varadan, and E. B. Makram, Harmonic Load Identification and Determination of Load Composition Using a Least Squares Method. Electric Power System Research, 37, 1996, 203-208
  • [17] W. M. Grady, L. A. Groce, T. M. Huebner, Q. C. Lu, and M. W. Crawford, Enhancement Implementation and Performance of an Adaptive Load Forecasting Technique, IEEE Trans. on Power System, 6, 1991, 450-456 [18] S. R. Huang, Short-Term Load Forecasting Using Threshold Autoregressive Models. IEE Proceedings: Generation, Transaction and Distribution, 144, 1997, 477-481
  • [19] B. J. Chen, M. W. Chang, and C. J. Lin, Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001, IEEE Trans. Power System, 19(4), 2004, 1821–1830.
  • [20] W. Jingmin, Z. Yamin and C. Xiaoyu C., Electricity Load Forecasting Based on Support Vector Machines and Simulated Annealing Particle Swarm Optimization Algorithm, Proceedings of the IEEE International Conference on Automation and Logistics August 18 - 21, Jinan, China.2007
  • [21] H. T. Yang and C. M. Huang, New Short Term Load-Forecasting Approach Using Self Organizing Fuzzy Armax Models., IEEE Transaction on Power System, 13, 1998, 217-225
  • [22] H. T. Yang, C. M. Huang, and C. L. Huang, Identification of Armax Model For Short Term Load Forecasting: An Evolutionary Programming Approach, IEEE Transactions on Power Systems, 11, 1996., 403-408
  • [23] X. Ma, A. A. El-Keib, R. E. Smith and H. Ma, Genetic Algorithm Based Approach to Thermal Unit Commitment of Electric Power Systems, Electric Power Systems Research, 34, 1995, 29-36
  • [24] R. S. Murphy, and J. S. Larry, Theory and Problems of Statistics, Third edition. McGraw-Hill. 1999, 280
  • [25] Central Bank of Nigeria Statistical Bulletin (Vol. 15, 16 and 17) November 2006, December 2009 and 2012 and National Bureau of Statistics (NBS), December 2008. Abuja, Nigeria.
  • [26] C. I. Dikio, J. O. Aderemi, and A. W. Biobele, Forecasting of Electrical Energy Demand in Nigeria using Modified form of Exponential Model. American Journal of Engineering Research (AJER). 7(1), 2018, 122 – 135
Year 2021, Volume: 5 Issue: 3, 71 - 85, 30.09.2021

Abstract

Project Number

936845

References

  • [1] U. P. Onochie, H. O. Egware, and T. O. Eyakwanor, The Nigeria Electric Power Sector, Opportunities and Challenges. Journal of Multidisciplinary Engineering Science and Technology (JMEST). Vol. 2 Issue 4, April – 2015.
  • [2] A. I. Obanor, An Intergrated and Pragmatic Approach to the Power Challenges in Nigeria. A lecture delivered at the 1st Public Lecture/Award Ceremony organized by Nigeria Institute of Mechanical Engineers (NIMechE) in Benin City, Edo State Nigeria. 2015.
  • [3] F. Adesola, A lecture on Data Capture Processing, 2006 Population & Housing Census of Nigeria, Dar-es-salaam, Tanzania. 2008.
  • [4] The Vanguard Nigeria Newspaper, February 1, 2019: http://www.vanguardngr.com/2019
  • [5] Worldometers, Department of economics and social affairs, population division. World population prospects, the 2019 Revision www.worldometers.info//
  • [6] UN Report, World population prospect: The 2017 Revision, www.vanguardngr.com/2017/06/nigeria-become-3rd-populous-country-2050-un-report/
  • [7] O. Obafemi et al (2018) IOP Conf. Ser.: Mater. Sci. Eng. 413 012053
  • [8] Nigerian Electricity Regulatory Commission. https://nerc.gov.ng
  • [9] N. Mellersh (2015), A Scramble for Power- The Nigeria Energy Crisis. African Law and Business” http://www.africanlawbusiness.com/news/5679 accessed on 27/05/2016
  • [10] The Nation Nigeria Newspaper May 10, 2019, https://thenationonlineng.net/2019/
  • [11] The Federal Ministry of Power Information Bureau – Abuja Nigeria, http://www.power.gov.ng/
  • [12] K. S. Arunesh, I. Khatoon, S. Muazzam, and D. K. Chaturvedi, Load Forecasting Techniques and Methodologies: A Review, International Conference on Power, Control and embedded systems. 2012, 631- 636
  • [13] G. Gross, and F. D. Galiana, Short Term Load Forecasting. Proceedings of the IEEE, Vol. 75, 1987, 1558-1573
  • [14] Q. Ding, Long-Term Load Forecast Using Decision Tree Method, Power Systems Conference and Exposition, PSCE 06, IEEE PES, Vol.1, 2006,1541-1543
  • [15] G. A. N. Mbamalu, and M. E. El-Hawary, Load Forecasting Via Suboptimal Seasonal Autoregressive Models and Iteratively Reweighted Least Squares Estimation. IEEE Transaction on Power System, 8, 1992,.343-348
  • [16] S. Varadan, and E. B. Makram, Harmonic Load Identification and Determination of Load Composition Using a Least Squares Method. Electric Power System Research, 37, 1996, 203-208
  • [17] W. M. Grady, L. A. Groce, T. M. Huebner, Q. C. Lu, and M. W. Crawford, Enhancement Implementation and Performance of an Adaptive Load Forecasting Technique, IEEE Trans. on Power System, 6, 1991, 450-456 [18] S. R. Huang, Short-Term Load Forecasting Using Threshold Autoregressive Models. IEE Proceedings: Generation, Transaction and Distribution, 144, 1997, 477-481
  • [19] B. J. Chen, M. W. Chang, and C. J. Lin, Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001, IEEE Trans. Power System, 19(4), 2004, 1821–1830.
  • [20] W. Jingmin, Z. Yamin and C. Xiaoyu C., Electricity Load Forecasting Based on Support Vector Machines and Simulated Annealing Particle Swarm Optimization Algorithm, Proceedings of the IEEE International Conference on Automation and Logistics August 18 - 21, Jinan, China.2007
  • [21] H. T. Yang and C. M. Huang, New Short Term Load-Forecasting Approach Using Self Organizing Fuzzy Armax Models., IEEE Transaction on Power System, 13, 1998, 217-225
  • [22] H. T. Yang, C. M. Huang, and C. L. Huang, Identification of Armax Model For Short Term Load Forecasting: An Evolutionary Programming Approach, IEEE Transactions on Power Systems, 11, 1996., 403-408
  • [23] X. Ma, A. A. El-Keib, R. E. Smith and H. Ma, Genetic Algorithm Based Approach to Thermal Unit Commitment of Electric Power Systems, Electric Power Systems Research, 34, 1995, 29-36
  • [24] R. S. Murphy, and J. S. Larry, Theory and Problems of Statistics, Third edition. McGraw-Hill. 1999, 280
  • [25] Central Bank of Nigeria Statistical Bulletin (Vol. 15, 16 and 17) November 2006, December 2009 and 2012 and National Bureau of Statistics (NBS), December 2008. Abuja, Nigeria.
  • [26] C. I. Dikio, J. O. Aderemi, and A. W. Biobele, Forecasting of Electrical Energy Demand in Nigeria using Modified form of Exponential Model. American Journal of Engineering Research (AJER). 7(1), 2018, 122 – 135
There are 25 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Oniyeburutan Ebakumo 0000-0002-7527-2436

Project Number 936845
Publication Date September 30, 2021
Published in Issue Year 2021 Volume: 5 Issue: 3

Cite

IEEE O. Ebakumo, “Electrical Energy Demand Forecast in Nigeria Between 2020 - 2040 Using Probabilistic Extrapolation Method”, IJESA, vol. 5, no. 3, pp. 71–85, 2021.

ISSN 2548-1185
e-ISSN 2587-2176
Period: Quarterly
Founded: 2016
Publisher: Nisantasi University
e-mail:ilhcol@gmail.com