IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/96903.html
   My bibliography  Save this paper

Modeling and forecasting demand for electricity in Zimbabwe using the Box-Jenkins ARIMA technique

Author

Listed:
  • NYONI, THABANI

Abstract

This study, which is the first of its kind in Zimbabwe, uses annual time series data on electricity demand in Zimbabwe from 1971 to 2014, to model and forecast the demand for electricity using the Box-Jenkins ARIMA framework. The study is guided by three objectives and these are: to analyze electricity consumption trends in Zimbabwe over the study period, to develop a reliable electricity demand forecasting model for Zimbabwe based on the Box-Jenkins ARIMA technique and last but not least, to project electricity demand in Zimbabwe over the next decade (2015 – 2025). Diagnostic tests indicate that X is an I (1) variable. Based on Theil’s U, the study presents the ARIMA (1, 1, 6) model, the diagnostic tests further show that this model is stable and hence suitable for forecasting electricity demand in Zimbabwe. The selected optimal model, the ARIMA (1, 1, 6) model proves beyond any reasonable doubt that in the next 10 years (2015 – 2025), demand for electricity in Zimbabwe will continue to fall. Amongst other policy recommendations, the study advocates for the liberalization of the electricity power sector in Zimbabwe in order to pave way for more efficient private investment whose potential is envisaged to adequately meet the existing demand for electricity.

Suggested Citation

  • Nyoni, Thabani, 2019. "Modeling and forecasting demand for electricity in Zimbabwe using the Box-Jenkins ARIMA technique," MPRA Paper 96903, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:96903
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/96903/1/MPRA_paper_96903.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hamzacebi, Coskun & Es, Huseyin Avni, 2014. "Forecasting the annual electricity consumption of Turkey using an optimized grey model," Energy, Elsevier, vol. 70(C), pages 165-171.
    2. Rob J Hyndman & Shu Fan, 2008. "Density forecasting for long-term peak electricity demand," Monash Econometrics and Business Statistics Working Papers 6/08, Monash University, Department of Econometrics and Business Statistics.
    3. Jingura, Raphael Muzondiwa & Musademba, Downmore & Kamusoko, Reckson, 2013. "A review of the state of biomass energy technologies in Zimbabwe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 652-659.
    4. Mulugetta, Yacob & Nhete, Tinashe & Jackson, Tim, 2000. "Photovoltaics in Zimbabwe: lessons from the GEF Solar project," Energy Policy, Elsevier, vol. 28(14), pages 1069-1080, November.
    5. Jingura, Raphael M. & Matengaifa, Rutendo, 2009. "Optimization of biogas production by anaerobic digestion for sustainable energy development in Zimbabwe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 1116-1120, June.
    6. Nyoni, Thabani, 2018. "Modeling and Forecasting Naira / USD Exchange Rate In Nigeria: a Box - Jenkins ARIMA approach," MPRA Paper 88622, University Library of Munich, Germany, revised 19 Aug 2018.
    7. Batidzirai, Bothwell & Lysen, Erik H. & van Egmond, Sander & van Sark, Wilfried G.J.H.M., 2009. "Potential for solar water heating in Zimbabwe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(3), pages 567-582, April.
    8. Lili Wang & Lina Zhan & Rongrong Li, 2019. "Prediction of the Energy Demand Trend in Middle Africa—A Comparison of MGM, MECM, ARIMA and BP Models," Sustainability, MDPI, vol. 11(8), pages 1-16, April.
    9. Taylor, James W., 2008. "An evaluation of methods for very short-term load forecasting using minute-by-minute British data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 645-658.
    10. Mungwena, Wilson, 2002. "The distribution and potential utilizablity of Zimbabwe's wind energy resource," Renewable Energy, Elsevier, vol. 26(3), pages 363-377.
    11. Nyoni, Thabani, 2018. "Box-Jenkins ARIMA approach to predicting net FDI inflows in Zimbabwe," MPRA Paper 87737, University Library of Munich, Germany.
    12. Hussain, Anwar & Rahman, Muhammad & Memon, Junaid Alam, 2016. "Forecasting electricity consumption in Pakistan: the way forward," Energy Policy, Elsevier, vol. 90(C), pages 73-80.
    13. Karin Kandananond, 2011. "Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach," Energies, MDPI, vol. 4(8), pages 1-12, August.
    14. Ömer Özgür Bozkurt & Göksel Biricik & Ziya Cihan Tayşi, 2017. "Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-24, April.
    15. Habeebur Rahman & Iniyan Selvarasan & Jahitha Begum A, 2018. "Short-Term Forecasting of Total Energy Consumption for India-A Black Box Based Approach," Energies, MDPI, vol. 11(12), pages 1-21, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aneeque A. Mir & Mohammed Alghassab & Kafait Ullah & Zafar A. Khan & Yuehong Lu & Muhammad Imran, 2020. "A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons," Sustainability, MDPI, vol. 12(15), pages 1-35, July.
    2. Gulay, Emrah & Duru, Okan, 2020. "Hybrid modeling in the predictive analytics of energy systems and prices," Applied Energy, Elsevier, vol. 268(C).
    3. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    4. Suat Ozturk & Feride Ozturk, 2018. "Forecasting Energy Consumption of Turkey by Arima Model," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 8(2), pages 52-60, February.
    5. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    6. Nyoni, Thabani, 2019. "Where is Eritrea going in terms of population growth? Insights from the ARIMA approach," MPRA Paper 92435, University Library of Munich, Germany.
    7. Changrui Deng & Xiaoyuan Zhang & Yanmei Huang & Yukun Bao, 2021. "Equipping Seasonal Exponential Smoothing Models with Particle Swarm Optimization Algorithm for Electricity Consumption Forecasting," Energies, MDPI, vol. 14(13), pages 1-14, July.
    8. Xingcai Zhou & Jiangyan Wang, 2021. "Panel semiparametric quantile regression neural network for electricity consumption forecasting," Papers 2103.00711, arXiv.org.
    9. Wang, Lin & Hu, Huanling & Ai, Xue-Yi & Liu, Hua, 2018. "Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm," Energy, Elsevier, vol. 153(C), pages 801-815.
    10. Hapanyengwi, Hamadziripi Oscar & Mutongi, Chipo & Nyoni, Thabani, 2019. "Understanding Inflation Dynamics in the kingdom of Eswantini: A Univariate Approach," MPRA Paper 94560, University Library of Munich, Germany, revised 18 Jun 2019.
    11. Nyoni, Thabani, 2019. "A Box-Jenkins ARIMA approach to the population question in Pakistan: A reliable prognosis," MPRA Paper 92434, University Library of Munich, Germany.
    12. Nyoni, Thabani, 2019. "Modeling and forecasting inflation in Tanzania using ARIMA models," MPRA Paper 92458, University Library of Munich, Germany.
    13. Nyoni, Thabani, 2019. "Can Algeria be the first African country to outsmart the Malthusian population trap? Insights from the ARIMA approach," MPRA Paper 92425, University Library of Munich, Germany.
    14. Roopnarain, Ashira & Adeleke, Rasheed, 2017. "Current status, hurdles and future prospects of biogas digestion technology in Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 1162-1179.
    15. Nyoni, Thabani, 2019. "ARIMA modeling and forecasting of Consumer Price Index (CPI) in Germany," MPRA Paper 92442, University Library of Munich, Germany.
    16. Nyoni, Thabani, 2019. "What will be Botswana's population in 2050? Evidence from the Box-Jenkins approach," MPRA Paper 93977, University Library of Munich, Germany.
    17. Nyoni, Thabani, 2019. "Modeling and forecasting remittances in Bangladesh using the Box-Jenkins ARIMA methodology," MPRA Paper 92463, University Library of Munich, Germany.
    18. Yi Liang & Dongxiao Niu & Ye Cao & Wei-Chiang Hong, 2016. "Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission," Energies, MDPI, vol. 9(11), pages 1-22, November.
    19. Peng Jiang & Jun Dong & Hui Huang, 2019. "Forecasting China’s Renewable Energy Terminal Power Consumption Based on Empirical Mode Decomposition and an Improved Extreme Learning Machine Optimized by a Bacterial Foraging Algorithm," Energies, MDPI, vol. 12(7), pages 1-24, April.
    20. Nyoni, Thabani, 2019. ""Incredible India"-an empirical confrimation from the Box-Jenkins ARIMA technique," MPRA Paper 96909, University Library of Munich, Germany.

    More about this item

    Keywords

    ARIMA; electricity consumption; electricity demand; energy; forecasting; Zimbabwe;
    All these keywords.

    JEL classification:

    • P28 - Political Economy and Comparative Economic Systems - - Socialist and Transition Economies - - - Natural Resources; Environment
    • P48 - Political Economy and Comparative Economic Systems - - Other Economic Systems - - - Legal Institutions; Property Rights; Natural Resources; Energy; Environment; Regional Studies
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:96903. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.