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Analyzing and Forecasting Electricity Consumption in Energy-intensive Industries in Rwanda

Author

Listed:
  • Daniel Mburamatare

    (College of Science and Technology, African Center of Excellence in Energy for Sustainable Development, University of Rwanda, Kigali, Rwanda,)

  • William K. Gboney

    (College of Science and Technology, African Center of Excellence in Energy for Sustainable Development, University of Rwanda, Kigali, Rwanda,)

  • Jean De Dieu Hakizimana

    (College of Science and Technology, African Center of Excellence in Energy for Sustainable Development, University of Rwanda, Kigali, Rwanda,)

  • Fidel Mutemberezi

    (College of Business and Economics, University of Rwanda, Kigali, Rwanda.)

Abstract

Accurate forecast in electricity consumption (EC) is of great importance for appropriate policy measures to be undertaken to avoid significant over or underproduction of electricity compared to the demand. This paper employs multiple regression (MLR) and autoregressive integrated moving average (ARIMA) for the econometric analysis. MLR has been used to investigate the impact of the potential economic factors that influence the consumption of electricity in energy-intensive industries while ARIMA is used for the electricity consumption forecasting from 2000 to 2026. ADF test has been applied to test for the unit-roots, the results show that all variables include a unit root on their levels but all series become stationary as a result of taking their first difference. Johansen technique and the Residuals based approach to testing for long-run relationships among variables has been used. The outcomes show that the variables are co-integrated. GDP per capita is statistically significant at a 1% level and EC decreases with higher GDP per capita. The results also show that EC increases with population, while Gross Capital Formation and Industry Value Added have less influence on EC. The ARIMA (1,1,1) was found to be the best model to forecast EC and the conclusion is provided.

Suggested Citation

  • Daniel Mburamatare & William K. Gboney & Jean De Dieu Hakizimana & Fidel Mutemberezi, 2022. "Analyzing and Forecasting Electricity Consumption in Energy-intensive Industries in Rwanda," International Journal of Energy Economics and Policy, Econjournals, vol. 12(1), pages 483-493.
    • Handle: RePEc:eco:journ2:2022-01-60
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Co-integration; Electricity Consumption; Forecasting; Industry Sector; Stationarity;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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