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Analysis of the Volatility of the Electricity Price in Kenya Using Autoregressive Integrated Moving Average Model

Received: 17 February 2015     Accepted: 4 March 2015     Published: 30 March 2015
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Abstract

Electricity has proved to be a vital input to most developing economies. As the Kenyan government aims at transforming Kenya into a newly-industrialized and globally competitive, more energy is expected to be used in the commercial sector on the road to 2030. Therefore, modelling and forecasting of electricity costs in Kenya is of vital concern. In this study, the monthly costs of electricity using Autoregressive Integrated Moving Average models (ARIMA) were used so as to determine the most efficient and adequate model for analysing the volatility of the electricity cost in Kenya. Finally, the fitted ARIMA model was used to do an out-off-sample forecasting for electricity cost for September 2013 to August 2016. The forecasting values obtained indicated that the costs will rise initially but later adapt a decreasing trend. A better understanding of electricity cost trend in the small commercial sector will enhance the producers make informed decisions about their products as electricity is a major input in the sector. Also it will assist the government in making appropriate policy measures to maintain or even lowers the electricity cost.

Published in Science Journal of Applied Mathematics and Statistics (Volume 3, Issue 2)
DOI 10.11648/j.sjams.20150302.14
Page(s) 47-57
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), 2015. Published by Science Publishing Group

Keywords

Electricity, ARIMA, Forecast, Kenya

References
[1] Allen, P. G. (1994). Economic Forecasting in Agriculture. International Journal of Forecasting, 4(10), 81-135.
[2] Box, G.E.P. and Jenkins, G.M. (1976). Time Series Analysis. San Francisco: San Francisco: Holden-Day.
[3] Central Bank of Kenya. (2015, March 03). CBK Forex Exchange Rates. Retrieved March 03, 2015, from CBK website: https://www.centralbank.go.ke
[4] Eberhard A. & K Gratwick. (2005). The Kenyan IPP Experience. South Africa: Working Paper No. 49, Management Programme in Infrastructure Reform and Regulation, Graduate School of Business, University of Cape Town.
[5] Government of Kenya. (1996). Economic Recovery Strategy for Wealth and Employment Creation 2003–2007. Nairobi: The Policy Framework Paper.
[6] Government of kenya. (2003). Economic Recovery Strategy for Wealth and Employment Creation 2003–2007. Nairobi.
[7] gregory and Adam. (1999). The Role of Vibrant Retail Electricity Markets in Assuring that Wholesale Power Markets Operate Effectively. electricity journal, 12(10), 61-73.
[8] Javier, C., Rosario, E., Francisco, J.N. and Antonio, J.C. (2003). ARIMA Models to Predict Next Day Electricity Prices. IEEE Transaction on Power Systems, 18(3), 1014-1015.
[9] Kenya National Bureau of Statistics. (2015, March 03). KNBS CPI and inflation rates for February 2015. Retrieved March 3, 2015, from KNBS website: http://www.knbs.or.ke/index.php?option=com_content&view=article&id=302:cpi-and-inflation-rates-for-february-2015&catid=82:news&Itemid=593
[10] Makridakis, S. and Hibon, M. (1995). ARMA Models and the Box Jenkins Methodology. Journal of Forecasting, 1(2), 111-153.
[11] Nyoike, P. (2002). ‘Is the Kenyan Electricity Regulatory Board Autonomous?’. Energy Policy(30), 987–997.
[12] Prerna, M. (2012). Forecasting Natural Gas Price-Time Series and Nonparametric Approach.
[13] Regulus. (2015, February 28). Electricity cost in Kenya. Retrieved March 03, 2015, from Electricity Cost website: https://stima.regulusweb.com/
[14] Syed, A.A., Muhammad, R., Amir, R. and Ammar, G.B. (2012). Impact of Oil prices on Food Inflation in Pakistan. Interdisciplinary Journal of Contemporary Research in Business, 13(11), 123-124.
Cite This Article
  • APA Style

    Mohammed Mustapha Wasseja, Samwel N. Mwenda. (2015). Analysis of the Volatility of the Electricity Price in Kenya Using Autoregressive Integrated Moving Average Model. Science Journal of Applied Mathematics and Statistics, 3(2), 47-57. https://doi.org/10.11648/j.sjams.20150302.14

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    ACS Style

    Mohammed Mustapha Wasseja; Samwel N. Mwenda. Analysis of the Volatility of the Electricity Price in Kenya Using Autoregressive Integrated Moving Average Model. Sci. J. Appl. Math. Stat. 2015, 3(2), 47-57. doi: 10.11648/j.sjams.20150302.14

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    AMA Style

    Mohammed Mustapha Wasseja, Samwel N. Mwenda. Analysis of the Volatility of the Electricity Price in Kenya Using Autoregressive Integrated Moving Average Model. Sci J Appl Math Stat. 2015;3(2):47-57. doi: 10.11648/j.sjams.20150302.14

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  • @article{10.11648/j.sjams.20150302.14,
      author = {Mohammed Mustapha Wasseja and Samwel N. Mwenda},
      title = {Analysis of the Volatility of the Electricity Price in Kenya Using Autoregressive Integrated Moving Average Model},
      journal = {Science Journal of Applied Mathematics and Statistics},
      volume = {3},
      number = {2},
      pages = {47-57},
      doi = {10.11648/j.sjams.20150302.14},
      url = {https://doi.org/10.11648/j.sjams.20150302.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20150302.14},
      abstract = {Electricity has proved to be a vital input to most developing economies. As the Kenyan government aims at transforming Kenya into a newly-industrialized and globally competitive, more energy is expected to be used in the commercial sector on the road to 2030. Therefore, modelling and forecasting of electricity costs in Kenya is of vital concern. In this study, the monthly costs of electricity using Autoregressive Integrated Moving Average models (ARIMA) were used so as to determine the most efficient and adequate model for analysing the volatility of the electricity cost in Kenya. Finally, the fitted ARIMA model was used to do an out-off-sample forecasting for electricity cost for September 2013 to August 2016. The forecasting values obtained indicated that the costs will rise initially but later adapt a decreasing trend. A better understanding of electricity cost trend in the small commercial sector will enhance the producers make informed decisions about their products as electricity is a major input in the sector. Also it will assist the government in making appropriate policy measures to maintain or even lowers the electricity cost.},
     year = {2015}
    }
    

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    AU  - Mohammed Mustapha Wasseja
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    DO  - 10.11648/j.sjams.20150302.14
    T2  - Science Journal of Applied Mathematics and Statistics
    JF  - Science Journal of Applied Mathematics and Statistics
    JO  - Science Journal of Applied Mathematics and Statistics
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    UR  - https://doi.org/10.11648/j.sjams.20150302.14
    AB  - Electricity has proved to be a vital input to most developing economies. As the Kenyan government aims at transforming Kenya into a newly-industrialized and globally competitive, more energy is expected to be used in the commercial sector on the road to 2030. Therefore, modelling and forecasting of electricity costs in Kenya is of vital concern. In this study, the monthly costs of electricity using Autoregressive Integrated Moving Average models (ARIMA) were used so as to determine the most efficient and adequate model for analysing the volatility of the electricity cost in Kenya. Finally, the fitted ARIMA model was used to do an out-off-sample forecasting for electricity cost for September 2013 to August 2016. The forecasting values obtained indicated that the costs will rise initially but later adapt a decreasing trend. A better understanding of electricity cost trend in the small commercial sector will enhance the producers make informed decisions about their products as electricity is a major input in the sector. Also it will assist the government in making appropriate policy measures to maintain or even lowers the electricity cost.
    VL  - 3
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Author Information
  • ICT directorate, Data processing section, Kenya National Bureau of Statistics, Nairobi-Kenya

  • ICT directorate, Data processing section, Kenya National Bureau of Statistics, Nairobi-Kenya

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