Using Machine-Learning Application to model the Causal Relationship between Electricity Consumption and Economic Growth in Nigeria
| International Journal of Economics and Management Studies |
| © 2023 by SSRG - IJEMS Journal |
| Volume 10 Issue 12 |
| Year of Publication : 2023 |
| Authors : Etim Akan, Alwell Nteegah, Barisua Nwinee |
10.14445/23939125/IJEMS-V10I12P101
How to Cite?
Etim Akan, Alwell Nteegah, Barisua Nwinee, "Using Machine-Learning Application to model the Causal Relationship between Electricity Consumption and Economic Growth in Nigeria," SSRG International Journal of Economics and Management Studies, vol. 10, no. 12, pp. 1-11, 2023. Crossref, https://doi.org/10.14445/23939125/IJEMS-V10I12P101
Abstract:
This study examines the correlation between electricity consumption and economic growth in Nigeria using timeseries data between 1990 and 2020. The study uses a Recurrent Neural Network (RNN), a machine learning application, to model the relationship between electricity consumption and Gross Domestic Product (GDP), representing the independent and dependent variables, respectively. A 30-year time series data for both variables forms the input data used in training the model and examining the causal relationship between the two variables. The study's outcome compares the actual GDP plot against the predicted values. The predicted GDP values track the actual GDP values with a high degree of accuracy. The RNN establishes a strong correlation between electricity consumption and economic growth in the country and provides an option for making a forecast of future GDP growth trends that are linked to an electricity supply scenario. There is a significant supply-demand gap in the electricity market in Nigeria, which calls for substantial investments to be made to achieve infrastructure upgrades, network expansion and improvement in service quality in the industry.
Keywords:
Correlation, Electricity, GDP, Macroeconomics, RNN.
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