International Journal of Energy and Power Engineering

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Analysis of Demand and Supply of Electrical Energy in Cameroon: Influence of Meteorological Parameters on the Monthly Power Peak of South and North Interconnected Electricity Networks

Received: 17 July 2014    Accepted: 28 July 2014    Published: 10 August 2014
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Abstract

Following the unbalanced provision between supply and demand of electrical energy in Cameroon, it is necessary to perform an analysis of the data since it can provide essential information for an optimal management of the power supply system. This study presents on the one hand an analysis of electrical energy demand and supply in Cameroon, and, on the other hand, the modeling of the monthly peak of the main interconnected network in Cameroon, namely South Interconnected Networks (RIS) and North (RIN) networks using econometrical methods. Meteorological parameters (monthly maximal temperatures and humidity) are considered as exogenous variables of this application. Following the seasonality observed during various months, the introduction of terms of monthly seasonal as well as an average coefficient Ci peculiar to each month will also be introduced into the linear regression model to evaluate the most suitable one for this modeling. From the above analysis, it appears that meteorological parameters have a significant influence on the monthly peak in both networks. As well as the coefficients of these parameters are not the most significant of the various models, the absence of these parameters in different models leads to an increase Akaike (AIC) and Schwartz (SC) criteria. However, the best model is based on the minimum AIC and SC. The monthly peak in both systems is observed at the same time (20h) and one a working day. This peak may be influenced by other parameters such as the return to households and their consumption pattern, the type of equipment they use amongst other.

DOI 10.11648/j.ijepe.20140304.12
Published in International Journal of Energy and Power Engineering (Volume 3, Issue 4, August 2014)
Page(s) 168-185
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Monthly Peak, Linear Regression Models, Meteorological Parameters, Network RIS and RIN, Modeling, Demand and Supply

References
[1] AES‐SONEL ,2011 Rapport AES/AREVA.
[2] Annuaire statistique 2010 du Cameroun – INS et Document de Stratégie pour la Croissance et l’Emploi- Rapport Définitif – Août 2009.
[3] A. Pardo, V. Meneu and E. Valor, “Temperature and Seasonality Influences on Spanish Electricity Load,” Energy Economics, Vol. 24, pp. 55-70, 2002.
[4] Box, George E. P. and Gwilym M. Jenkins (1976).Time Series Analysis: Forecasting and Control, Revised Edition, Oakland, CA: Holden-Day.
[5] C. Adjamagbo, P. Ngae, A. Vianouet V. Vigneron « Modélisation de la demande en énergie électrique au Togo » Revue des Energies Renouvelables Vol. 14 N°1 (2011) 67 – 67.
[6] Context of Auto-Correlated Errors,” Journal of the American Statistical Association, 64, 253–272.
[7] D. H. W. Li, J. C. Lam, and S. L. Wong, “Daylightingand Its Effects on Peak Load Determination,” Energy, Vol. 30, pp. 1817-1831, 2005.
[8] EViews. http://www.eviews.com
[9] Fair, Ray C. (1984). Specification, Estimation, and Analysis of Macroeconometric Models, Cambridge, MA: Harvard University Press.
[10] F. Egelioglu, A. A. Mohamad, and H. Guven, “Economic Variables and Electricity Consumption in Northern Cyprus,” Energy, Vol. 26, pp. 355-362, 2001.
[11] Greene, William H. (2008). Econometric Analysis, 6th Edition, Upper Saddle River, NJ: Prentice-Hall.
[12] Hamilton, James D. (1994a). Time Series Analysis, Princeton University Press.
[13] Hayashi, Fumio. (2000). Econometrics, Princeton, NJ: Princeton University Press.
[14] Johnston, Jack and John Enrico DiNardo (1997). Econometric Methods, 4th Edition, New York: McGrawHill.
[15] M. A. Almeida, R. Schaeffer and E. L. La Rovere, “The Potential for Electricity Conservation and Peak Load Reduction in the Residential Sector of Brazil,” Energy , Vol. 26, pp. 413-429, 2001.
[16] M. A. Momani, “Factors Affecting Electricity Demand in Jordan,” Energy and Power Engineering, Vol. 5, No. 1, pp. 50-58, 2013.
[17] S. B. Sadineni, and R. F. Boehm, “Measurements and Simulations for Peak Electrical Load Reduction in Cooling Dominated Climate,” Energy, Vol. 37, pp. 689-697, 2012.
[18] S. Dudhani, A. K. Sinha, and S. S. Inamdar, “Renewable Energy Sources for Peak Load Demand Management in India,” Electrical Power and Energy Systems, Vol. 28, pp. 396-400, 2006.
[19] Wadjamsse Beaudelaire Djezou, PhD, “Analyse des déterminants de l’efficacité énergétique dans l’espace UEMOA” European Scientific Journal April 2013 edition vol.9, No.12 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431.
[20] Y. S. Akil, and H. Miyauchi, “Seasonal Regression Models for Electricity Consumption Characteristics Analysis,” Engineering, Vol. 5, No. 1B, pp. 108-114, 2013.
[21] Yusri Syam Akil, Hajime Miyauchi. Seasonal Peak Electricity Demand Characteristics: Japan Case Study. International Journal of Energy and Power Engineering. Vol. 2, No. 3, 2013, pp. 136-142. doi: 10.11648/j.ijepe.20130203.18.
[22] SIE-Cameroon, 2009. Cameroon energy information system: Report 2009. Ministryof Energy and Water resources.
[23] SIE-Cameroon, 2010. Cameroon energy information system: Report 2010. Ministryof Energy and Water resources.
[24] R. Starts, EViews Illustrated for Version 7.2, 1st Ed., (2012) Micro Software, LLC, 2012.
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  • APA Style

    Flora Isabelle Métégam Fotsing, Donatien Njomo, Réné Tchinda. (2014). Analysis of Demand and Supply of Electrical Energy in Cameroon: Influence of Meteorological Parameters on the Monthly Power Peak of South and North Interconnected Electricity Networks. International Journal of Energy and Power Engineering, 3(4), 168-185. https://doi.org/10.11648/j.ijepe.20140304.12

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    ACS Style

    Flora Isabelle Métégam Fotsing; Donatien Njomo; Réné Tchinda. Analysis of Demand and Supply of Electrical Energy in Cameroon: Influence of Meteorological Parameters on the Monthly Power Peak of South and North Interconnected Electricity Networks. Int. J. Energy Power Eng. 2014, 3(4), 168-185. doi: 10.11648/j.ijepe.20140304.12

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    AMA Style

    Flora Isabelle Métégam Fotsing, Donatien Njomo, Réné Tchinda. Analysis of Demand and Supply of Electrical Energy in Cameroon: Influence of Meteorological Parameters on the Monthly Power Peak of South and North Interconnected Electricity Networks. Int J Energy Power Eng. 2014;3(4):168-185. doi: 10.11648/j.ijepe.20140304.12

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  • @article{10.11648/j.ijepe.20140304.12,
      author = {Flora Isabelle Métégam Fotsing and Donatien Njomo and Réné Tchinda},
      title = {Analysis of Demand and Supply of Electrical Energy in  Cameroon: Influence of Meteorological Parameters on the Monthly Power Peak of South and North Interconnected Electricity Networks},
      journal = {International Journal of Energy and Power Engineering},
      volume = {3},
      number = {4},
      pages = {168-185},
      doi = {10.11648/j.ijepe.20140304.12},
      url = {https://doi.org/10.11648/j.ijepe.20140304.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20140304.12},
      abstract = {Following the unbalanced provision between supply and demand of electrical energy in Cameroon, it is necessary to perform an analysis of the data since it can provide essential information for an optimal management of the power supply system. This study presents on the one hand an analysis of electrical energy demand and supply in Cameroon, and, on the other hand, the modeling of the monthly peak of the main interconnected network in Cameroon, namely South Interconnected Networks (RIS) and North (RIN) networks using econometrical methods. Meteorological parameters (monthly maximal temperatures and humidity) are considered as exogenous variables of this application. Following the seasonality observed during various months, the introduction of terms of monthly seasonal as well as an average coefficient Ci peculiar to each month will also be introduced into the linear regression model to evaluate the most suitable one for this modeling. From the above analysis, it appears that meteorological parameters have a significant influence on the monthly peak in both networks. As well as the coefficients of these parameters are not the most significant of the various models, the absence of these parameters in different models leads to an increase Akaike (AIC) and Schwartz (SC) criteria. However, the best model is based on the minimum AIC and SC. The monthly peak in both systems is observed at the same time (20h) and one a working day. This peak may be influenced by other parameters such as the return to households and their consumption pattern, the type of equipment they use amongst other.},
     year = {2014}
    }
    

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  • TY  - JOUR
    T1  - Analysis of Demand and Supply of Electrical Energy in  Cameroon: Influence of Meteorological Parameters on the Monthly Power Peak of South and North Interconnected Electricity Networks
    AU  - Flora Isabelle Métégam Fotsing
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    JF  - International Journal of Energy and Power Engineering
    JO  - International Journal of Energy and Power Engineering
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    PB  - Science Publishing Group
    SN  - 2326-960X
    UR  - https://doi.org/10.11648/j.ijepe.20140304.12
    AB  - Following the unbalanced provision between supply and demand of electrical energy in Cameroon, it is necessary to perform an analysis of the data since it can provide essential information for an optimal management of the power supply system. This study presents on the one hand an analysis of electrical energy demand and supply in Cameroon, and, on the other hand, the modeling of the monthly peak of the main interconnected network in Cameroon, namely South Interconnected Networks (RIS) and North (RIN) networks using econometrical methods. Meteorological parameters (monthly maximal temperatures and humidity) are considered as exogenous variables of this application. Following the seasonality observed during various months, the introduction of terms of monthly seasonal as well as an average coefficient Ci peculiar to each month will also be introduced into the linear regression model to evaluate the most suitable one for this modeling. From the above analysis, it appears that meteorological parameters have a significant influence on the monthly peak in both networks. As well as the coefficients of these parameters are not the most significant of the various models, the absence of these parameters in different models leads to an increase Akaike (AIC) and Schwartz (SC) criteria. However, the best model is based on the minimum AIC and SC. The monthly peak in both systems is observed at the same time (20h) and one a working day. This peak may be influenced by other parameters such as the return to households and their consumption pattern, the type of equipment they use amongst other.
    VL  - 3
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Author Information
  • Environmental Energy Technologies Laboratory (EETL), University of Yaounde I, PO Box 812, Yaounde, Cameroon; Laboratory of Industrial Systemsand Environment of the University of Dschang, PO BOX 96, Dschang, Cameroon

  • Environmental Energy Technologies Laboratory (EETL), University of Yaounde I, PO Box 812, Yaounde, Cameroon

  • Laboratory of Industrial Systemsand Environment of the University of Dschang, PO BOX 96, Dschang, Cameroon

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