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

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“…In order to analyze the stationarity and the existence of unit root in the dataset, both the first-generation unit root tests, such as the Levin-Lin-Chu (LLC) test and Fisher Augmented Dickey Fuller (Fisher ADF) test, and the second-generation unit root tests, such as the Pesaran cross-sectionally ADF (CADF) and cross-sectionally augmented panel unit root test (CIPS) tests, were performed, in line with the work of Jan et al (2021), Zaidi et al (2021), andMburamatare et al (2022). The results of both first-generation panel unit root tests show that variables real_ gdp_cap and renewable are non-stationary at level, the same observation can be made for trade and gov under the Fisher ADF unit root test, while the rest of the variables are stationary at level (Table 6).…”