Electricity demand forecasting in case of Uzbekistan
DOI:
https://doi.org/10.5281/zenodo.15462182Ключевые слова:
Electricity demand, ARIMAX, Uzbekistan, GDP growth, Population.Аннотация
This study is aimed to forecast demand for electricity in case of Uzbekistan in medium term with the influences
of economic growth and population growth. For forecasting the demand for electricity, ARIMAX model is employed as
time series data is utilized for the study. In response to preliminary findings, the economic growth and population growth
are argued as one of the major sources and factors to rise demand for electricity, but the question was by how much
those factors lead to growth of electricity demand in case of Uzbekistan. Thus, by taking into account those factors, the
electricity demand has been struggled to forecast by 2027 based on the past data from 2010 till 2023.
Библиографические ссылки
International Energy Agency (IEA), 2024. Uzbekistan Energy Outlook: Electricity Demand Trends and Forecasts.
Available at: https://www.iea.org [Accessed 31 March 2025].
World Bank, 2023. Uzbekistan: Energy Consumption and Economic Growth Data. Available at: https://www.worldbank.
org [Accessed 31 March 2025].
Asian Development Bank (ADB), 2023. Power Sector Development in Uzbekistan: Investments and Reforms. Available
at: https://www.adb.org [Accessed 31 March 2025].
Ministry of Energy of Uzbekistan, 2024. Energy Sector Reforms and Future Projections. Available at: https://minenergy.
uz [Accessed 31 March 2025].
Statistical Committee of Uzbekistan, 2024. Annual Electricity Consumption and Production Reports. Available at:
https://stat.uz [Accessed 31 March 2025].
Baumeister, C. & Kilian, L. (2016). ‘Understanding the Decline in the Price of Oil Since June 2014’, Journal of the
Association of Environmental and Resource Economists, 3(1), pp. 131-158.
Bianco, V., Manca, O., & Nardini, S. (2009). ‘Electricity consumption forecasting in Italy using linear regression models’,
Energy, 34(9), pp. 1413-1421.
Zhang, Y., Li, C., & Zhang, M. (2018). ‘A Hybrid ARIMA-SVM Model for Electricity Demand Forecasting in China’,
Energy Policy, 123, pp. 412-421.
Adebayo, S., Adekunle, A., & Ojo, T. (2019). ‘Forecasting Nigeria’s Electricity Demand Using Bayesian Structural Time
Series’, Renewable Energy, 135, pp. 1372-1381.
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