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

Daniel Mburamatare, William K. Gboney, Jean De Dieu Hakizimana and Fidel Mutemberezi
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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.

International Journal of Energy Economics and Policy, 2022, vol. 12, issue 1, 483-493

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.

Keywords: Co-integration; Electricity Consumption; Forecasting; Industry Sector; Stationarity (search for similar items in EconPapers)
JEL-codes: C22 C52 E17 (search for similar items in EconPapers)
Date: 2022
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