Analyzing and Forecasting Electricity Consumption in Energy-intensive Industries in Rwanda
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 (ARI...
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| Format: | Article |
| Language: | English |
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EconJournals
2022-01-01
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| Series: | International Journal of Energy Economics and Policy |
| Subjects: | |
| Online Access: | https://econjournals.com/index.php/ijeep/article/view/12526 |
| _version_ | 1797909643442257920 |
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| author | Daniel Mburamatare William K. Gboney Jean De Dieu Hakizimana Fidel Mutemberezi |
| author_facet | Daniel Mburamatare William K. Gboney Jean De Dieu Hakizimana Fidel Mutemberezi |
| author_sort | Daniel Mburamatare |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T11:11:34Z |
| format | Article |
| id | doaj.art-c18d82adc9b546cd909e1ec8de32aa59 |
| institution | Directory Open Access Journal |
| issn | 2146-4553 |
| language | English |
| last_indexed | 2024-04-10T11:11:34Z |
| publishDate | 2022-01-01 |
| publisher | EconJournals |
| record_format | Article |
| series | International Journal of Energy Economics and Policy |
| spelling | doaj.art-c18d82adc9b546cd909e1ec8de32aa592023-02-15T16:19:07ZengEconJournalsInternational Journal of Energy Economics and Policy2146-45532022-01-0112110.32479/ijeep.12526Analyzing and Forecasting Electricity Consumption in Energy-intensive Industries in RwandaDaniel Mburamatare0William K. Gboney1Jean De Dieu Hakizimana2Fidel Mutemberezi3College of Science and Technology, African Center of Excellence in Energy for Sustainable Development, University of Rwanda, Kigali, RwandaCollege of Science and Technology, African Center of Excellence in Energy for Sustainable Development, University of Rwanda, Kigali, RwandaCollege of Science and Technology, African Center of Excellence in Energy for Sustainable Development, University of Rwanda, Kigali, RwandaCollege of Business and Economics, University of Rwanda, Kigali, RwandaAccurate 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.https://econjournals.com/index.php/ijeep/article/view/12526Electricity consumptionCo-integrationindustry sectorstationarityforecasting |
| spellingShingle | Daniel Mburamatare William K. Gboney Jean De Dieu Hakizimana Fidel Mutemberezi Analyzing and Forecasting Electricity Consumption in Energy-intensive Industries in Rwanda International Journal of Energy Economics and Policy Electricity consumption Co-integration industry sector stationarity forecasting |
| title | Analyzing and Forecasting Electricity Consumption in Energy-intensive Industries in Rwanda |
| title_full | Analyzing and Forecasting Electricity Consumption in Energy-intensive Industries in Rwanda |
| title_fullStr | Analyzing and Forecasting Electricity Consumption in Energy-intensive Industries in Rwanda |
| title_full_unstemmed | Analyzing and Forecasting Electricity Consumption in Energy-intensive Industries in Rwanda |
| title_short | Analyzing and Forecasting Electricity Consumption in Energy-intensive Industries in Rwanda |
| title_sort | analyzing and forecasting electricity consumption in energy intensive industries in rwanda |
| topic | Electricity consumption Co-integration industry sector stationarity forecasting |
| url | https://econjournals.com/index.php/ijeep/article/view/12526 |
| work_keys_str_mv | AT danielmburamatare analyzingandforecastingelectricityconsumptioninenergyintensiveindustriesinrwanda AT williamkgboney analyzingandforecastingelectricityconsumptioninenergyintensiveindustriesinrwanda AT jeandedieuhakizimana analyzingandforecastingelectricityconsumptioninenergyintensiveindustriesinrwanda AT fidelmutemberezi analyzingandforecastingelectricityconsumptioninenergyintensiveindustriesinrwanda |