Forecast on 2030 Vietnam Electricity Consumption


  • V. H. M. Nguyen Faculty of Electrical and Electronics Engineering, HCMC University of Technology and Education, Vietnam
  • K. T. P. Nguyen Department of Research and Development, Global Electrical Technology Corporation, Vietnam
  • C. V. Vo Faculty of Electrical and Electronics Engineering, HCMC University of Technology and Education, Vietnam
  • B. T. T. Phan Faculty of Electrical and Electronics Engineering, HCMC University of Technology, Vietnam


The first but very significant step in electricity system planning is to make an accurate long-term forecast on electricity consumption. This article aims to forecast the consumption for the Vietnam electricity system (GWH) up to 2030. An econometric model with the Cobb Douglas production function is used. The five variables proposed in the forecasting function are GDP, income, population, proportion of industry and service in GDP, and number of households. The forecasting equation is tested in terms of stationary and co-integration to choose meaningful variables and to eliminate the minor ones which account for none or small impacts on the forecast. The results show that: (1) the qualified forecasting equation only includes 3 major variables: the per capita income, the population, and the number of households, (2) with the medium income scenario, the forecasting consumptions in 2020, 2025, 2030 are 230,195 GWH, 349,949 GWH, 511,268 GWH respectively. (3) The GDP and the proportion of industry and service in GDP do not make major impacts on this forecasting in Vietnam. The method and the result of this article are likely a typical example of forecasting electricity consumption in developing countries which have a transforming economy similar to that in Vietnam.


long-term, forecasting, electricity, consumption, econometric, model, Cobb Douglas, production, function, Vietnam


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How to Cite

V. H. M. Nguyen, K. T. P. Nguyen, C. V. Vo, and B. T. T. Phan, “Forecast on 2030 Vietnam Electricity Consumption”, Eng. Technol. Appl. Sci. Res., vol. 8, no. 3, pp. 2869–2874, Jun. 2018.


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