Mô hình kinh tế lượng dự báo nhu cầu điện năng của ngành công nghiệp và xây dựng tại Việt Nam đến 2030

Science & Technology Development Journal – Engineering and Technology, 3(1):316-325 Open Access Full Text Article Research Article 1Ho Chi Minh City University of Technology and Education, No.1, Vo Van Ngan Street, HCMC, Vietnam 2Tien Giang University, No.119, Ap Bac Street, Ward 5, MyTho, Tien Giang 3University of Architecture Ho Chi Minh City, No 196, Pasteur Street, District 3, HCMC, Vietnam Correspondence Viet-Cuong VO, Ho Chi Minh City University of Technology and Education, No.1, Vo Van Ngan Street, HCMC, Vietnam Email: cuongvv@hcmute.edu.vn History  Received: 15-12-2019  Accepted: 13-3-2020  Published: 31-3-2020 DOI : 10.32508/stdjet.v3i1.646 Copyright © VNU-HCM Press. This is an open- access article distributed under the terms of the Creative Commons Attribution 4.0 International license. Econometric model for forecasting electricity demand of industry and construction sectors in Vietnam to 2030 Viet-Cuong VO1,*, Hoang-Phuong NGUYEN1,2, Le-Duy-Luan NGUYEN3, Van-Hung PHAM1 Use your smartphone to scan this QR code and download this article ABSTRACT An accurate forecasting for long-term electricity demand make a major role to the planning of the power system in any country. Vietnam is one of the most economic developing country in the world, and its electricity demand has been increased dramatically high of about 15%/y for last three decades. Contribution of industry and construction sectors in GDP have been increasing year by year, and are currently holding the leading position of the largest consumers with more than 50% sharing in national electricity consumption proportion. How to estimate correctly electricity con- sumption of these sectors takes a crucial contribution for the planing of the power system. This paper applies econometric model with Cobb Douglas production function - a top down method to forecast electricity demand of the industry and construction sectors in Vietnam to 2030. Four variables used are value of the sectors in GDP, income per person, proportion of electricity con- sumption of the sectors in total, and electric price. Forecasted results show that the proposed method has a quite low MAPE of 7.66% for a long-term forecating. Variable of electric price does not affect to the demand. This is a very critical result of the study for authority governors in Vietnam. In base scenario of the GDP and the income per person, the forecated electricity demands of the sectors are112,853 GWh, 172,691 GWh, and 242,027 GWh in 2020, 2025, 2030, respectively. In high screnario one, the demands are 115,947 GWh, 181,591 GWh, and 257,272 GWh, respectively. The above vaules in the high scenario are less than from 9.0% to 15.8 % of that of the based of in the Revised version of master plan N0. VII. Keywords: Forecast, Econometric model, Electricity demand, Cobb Douglas production function, Industry & construction sectors INTRODUCTION An accurate forecasting for long-term electric loads can make a major contribution to the planning of a national power system. Forecasting electric load de- mand in considerationwith its definite duration could be applied to build up long-term maintenance sched- ules, to establish the development and construction plan for new power-facilities, and expansion plan for transmission and distribution systems. The accuracy of a long-term forecasting model will lead to the fea- sibility and rationality of the future production and distribution development plan of power enterprises. An overestimating of demand can result unexpected effects on financial terms; while an underestimating will not only lead to the lack of power supply, but also injure the satisfactory of consumers or even damage to the national economy and society. Therefore, fore- casting power demand for industry and construction sectors is an important compulsory. Industry and construction sectors are the two biggest sectors which contribute more than 40% of Vietnam’s gross domestic production (GDP). In order to meet that contribution, more than 50%ofVietnam electric- ity consumption has been delivered to that two sec- tors; in which some huge consumers could be identi- fied as cement industry, steel - making factories, food processing facilities and beverage manufacturers, tex- tile industry, etc Consumption of those industries has been increased significantly in recent years1. Worldwide-studies on forecasting long-term electric load demand over the last decades are mainly fo- cused on applying artificial neural network (ANN- based models)2, and econometric model (i.e. Cobb Douglas) into planning process3. ANN-basedmodels have been implemented successfully in many coun- tries due to its flexibility, high accuracy in terms of forecasting, good adaptation and processing abil- ity on fluctuant data. However, when applying into Vietnam’s context, ANN cannot prove its mentioned strengths because historical data on national and par- tial electric consumption was not fully recorded. Cobb Douglas production function is an econometric approach which combines different statistical tech- niques and economic theories to forecast long-term Cite this article : VO V, NGUYEN H, NGUYEN L, PHAM V. Econometric model for forecasting electricity demand of industry and construction sectors in Vietnam to 2030. Sci. Tech. Dev. J. – Engineering and Technology; 3(1):316-325. 316 Science & Technology Development Journal – Engineering and Technology, 3(1):316-325 power demand and electricity consumption. This method has been widely applied in researches of de- veloping countries, such asMalaysia4, Pakistan5, and China6, etc. The strengths of Cobb Douglas pro- duction function are that: (1) it provides detail in- formation about the stable and the changeability of variables and forecasted values; (2) it could be used to investigate the influential factors or variables im- pacting on forecasted results; and (3) it is a practi- cal method which is easy to be analyzed and calcu- lated 7. Despite all these advantages, there is no record about experience on applying thismethod in forecast- ing long-term electricity demand for the industry and construction sectors. METHODANDDATA Cobb Douglas production function A Cobb Douglas production function is expressed in a non-linear form as2: ECt = jG b1 t I b2 t X b3 t P b4 t (1) Where ECt is the consumption of industry and con- struction sectors in the year t; Gt , It , Xt , and Pt are represented for the GDP of industry and construction sectors, income per person (US$/person), proportion of industry and construction sectors’ electricity con- sumption in total, and electric tariff in the year t, re- spectively. The technological parameter is indicated by j ; b i are returns to scale linked with the four vari- ables. A logarithmic transformation is applied for two sides of the equation (1) to linearize it to become a linear form as: lnECt = b0 + b1 lnGt + b2 ln It + b3 lnXt + b4 lnPt (2) Testing In other to evaluate the accuracy of econometric model, many testing methods have been recorded. Testingmethods are computedwith aims tomaximize the accuracy and reliability of forecasting equation. For those researches proving the causality between variables, the date in time series should be tested the stationary (Augmented Dickey Fuller (ADF) or Phillips-Perron (P-P)) to avoid the spurious regres- sion. After stationary test, the causality between vari- ables is proven by using Granger causality test. For forecasting purpose, stationary test only is enough. Stationary test In order to ensure the sustainability of this study, two unit root test namely ADF and P-P are employed to test the stationary of prediction function. They are employed to avoid the spurious regression or non- sense regression when applying regression algorithm onto a non-stationary time series data. If there are some non-stationary series, to solve the problem, the first difference (D) of the series is done, then elimi- nating inappropriate variables. If all series are non- stationary, it leads to take the first difference of all variables, then removes the primary features of the se- ries. However, it leads to the low R2 in regressing. The low R2 means the low accuracy of the prediction. To avoid the low of R2, co-integration test is conducted. Cointegration test Engle and Granger (1987)8 supposed the linear com- bination between non-stationary time series can be a stationary series and these non-stationary time series are co-integrated. The non-stationary linear combi- nation is called co-integration equation and can be ex- plained as the balance relationship between variables in long-term. Therefore, if variables co-integrate, the regression can be conducted. In this article, Jo- hansen trace test is utilized to test the co-integration. In the Trace test (T-test), there are some hypotheses H0 listed as follow: “None”, means there is no co- integration; “At most 1; 2; 3”, means there is 1, or 2, or 3 co-integrations. To decide if we should reject or accept hypothesis H0, Trace Statistic value and Criti- cal Value of 5% are compared. If Trace Statistic < Crit- ical Value, acceptH0; if Trace Statistic >Critical Value, reject H0. R2 test (R square) and p-test R2 plays an important role to evaluate the impact of dependent variables to independent variables. The condition to choose the impact factors of function is that R2 is approximately 1. For example, if the value of R2 is 0.997953, then it means that 99.8% dependent variables have impact on independent ones. How- ever, in order to decide if any independent variable is needed to function, then a p-value test must be con- ducted. The condition to choose the value of p-value is p-value  0.05. If any independent variable has its p-value bigger than 0.05 (p > 0.05), then that variable must be eliminated and testing process must be iter- ated to get higher accurate forecasted results. MAPE test Beside the stationary and co-integration test, a feasi- bility test based on historical time-series data of pre- diction function will be conducted by employing a Mean Absolute Percentage Error (MAPE) method to measure the accuracy of prediction. It has a fact that 317 Science & Technology Development Journal – Engineering and Technology, 3(1):316-325 a relative error of a forecast can be measured by a MAPE, which could be expressed as: MAPE = 1Nå N n=1 ANFNAN 100% (3) WhereAn is recorded consumption of the year n; Fn is forecasted consumption of the year n. It is noted that MAPE has an opposite principle with R2 as a lower computed MAPE will lead to higher reliable predic- tion, while a higher R2 could act the same meaning. MAPE is launched in this paper to evaluate the accu- racy of forecast function and to compare with toler- ance indicator values. Forecasting After building a standard function for predicting the electricity consumption ofVietnam industry and con- struction sectors, variable data and its correspond- ing historical data will then be obtained to import to the function. The consumption demand of Vietnam industry and construction sectors in the year 2020, 2025, and 2030 will be forecasted correspondingly af- terward. Data GDP of industry and construction sectors Vietnam is remarked as one of the most rapid grow- ing countries in the world with the GDP growth rate at about 6.5%/year in recent years; in which indus- try and construction sectors is the biggest contribu- tor with the GDP growth rate is still maintained at a higher rate than the GDP of the entire economics. In 2011, the GDP growth rate of industry and construc- tion sectors reached 6.68% while the value of the na- tional economics is recorded at 6.24%; corresponding values in 2012 are 5.75% and 5.25%, 5.5% and 5.4% in 2013. The average values of the last three years are 6% and 5.4%, correspondingly. There are three grow- ing scenarios for Vietnam’s GDP to 2035 have been released; in which the GDP growth rates in the low scenario are forecasted to reach 6.5%/year in 2016- 2025 and 6%/year from 2026 to 2035 9. These results are closely similar to forecasted growth rate of elec- tricity consumption demands which are released by the Revised version of Master Plan N0. VII for Power System in Vietnam (PDP.VII rev.)9. This similarity can be used to demonstrated that GDP and electricity consumption demand could have a somehow corre- lation. The GDP data of industry and construction sectors will be cited from the PDP.VII rev. Income Income per person and person’s electricity demand are confidentially believed to have a linear relation as the increase of person’s incomes will lead to the higher needs of electric appliances and corresponding con- sumptions to improve the living conditions. For ex- ample at a low income condition, air conditioner and electric water heater could be dispensable. However, when the income is improved on enough to cover a high-cost electric bill, then they might be purchased to meet the demand of the house owners. It definitely leads to the more pressure on electric supply condi- tions. Therefore, the person’s income is launched as an essential variable of forecastingmodel in this paper to quantify its relation with electricity demand10. The income per capita data will be collected from World Bank data source11. Proportion of industry and construction sec- tors’s electricity consumption in total The 2017 report of EVN highlighted a rapid in- crease of proportion in total of electricity consump- tion needed for the industry, and construction sec- tors. It has been recorded to increase from 46.7% in 2005 to 51.9% in 2010, and to 54.77% in 2015. Also, it is remarked that the growth rate in the duration of 2010-2015 is lower than the ones recorded in the pre- vious duration (2005-2010). However, the industry and construction sectors are still acting as the leading sector and contribute at a highest level in compari- son with other sectors in economics. Data relevant to proportion of electricity consumption of industry and construction sectors are be referred from the PDP.VII rev. Electric tariff Electric tariff is an essential condition which impacts directly on every production in economics. In other words, electric tariff plays as a major input manufac- turing cost of all sectors in economics. A number of studies on the impacts of electric tariff onto consump- tion behaviors have already been deployed in the last decades. However, it is really difficult to get an una- nimity between those studies as the tariff policy is dif- fered from countries to countries. Vietnam’s electric tariff is identified as the lowest fare level in region and worldwide. Data involving national electric tariff will be obtained from EVN’s database 9. Collecting data in time series of 1990 to 2015 After analyzing factors which could make influence on the electricity consumption of industry and con- struction sectors in Vietnam, then the historical records of those factors are assembled from various 318 Science & Technology Development Journal – Engineering and Technology, 3(1):316-325 database sources. The more sufficient, reliable and long enough data, themore accurate forecasted results are. With a given time series data and k represents for an independent variable of prediction model, then (n – k) > 20 12,13. In this paper, GDP of industry and construction sec- tors, the proportion of industry and construction sec- tors in entire economics, income of person, and elec- tric tariff will be brought into forecasting model as in- dependent variables. Four independent variables will lead to n > 20 + 4. In this paper, historical data from 1990 to 2015 are as- sembled and constructed as a time series order. This selected 26 values (equivalent to 26 years) time series is relatively long enough to be tested. The collected data are shown in Table 1. RESULTS Converting variables into natural loga- rithms The input time series data will be converted into nat- ural logarithm forms. Converted results are shown in Table 2. Testing Stationary test A stationary test is conducted to test the stationary characteristics of data. Results shown in Table 3 in- dicate that all variables are non-stationary. As shown in Table 3, the p-value of almost variables are higher than a (a = 0.05). The only variable has the p-value of lower than 0.05 is the GDP of industry and con- struction sectors [ln(Gt )]. As all variables are non- stationary, a co-integration test will be employed to be conducted instead of computing a first difference calculation. Co-integration test Results of co-integration test are performed in Ta- ble 4. The T-test results indicate that there are only four co-integration variables at the value of 0.05. It is corresponding to the four mentioned non-stationary time series data. R2 and p-value testing After conducting co-integration test, the coefficients of equation (2) are computed. Calculated results are performed in Table 5. The condition for choosing the coefficients of equation is that the R2 value is approxi- mately 1 and p-value of all variables are less than 0.05. Table 5 shows the value of R2 testing of 0.992581. It means that 99.3% of dependent variables have im- pacts on independent ones. However, as p-value of lnPt is above 0.05, lnPt is eliminated. Then all coeffi- cients of equation will be recalculated in second time. Recalculated results are shown in Table 6; in which p-value of all variables are less than 0.05. Eliminating inappropriate variables As lnPt is eliminated, the equation (2) become: lnECt = b0+b1 lnGt +b2 ln It +b3 lnXt (4) With b 0, b 1, b 2, and b 3 are -5.05, 0.372, 0.623, and 1.864, respectively. Then the equation (4) becomes: lnECt = 5:05 + 0:372lnGt + 0:623ln It + 1:864lnXt (5) That equation (5) is the qualified equation for fore- casting. Comparison to historical data and evaluat- ingMAPE The equation (5) is launched to recalculate the elec- tricity consumption in the past. Results are brought into a comparison with historical data to evaluate the accuracy of forecasting. Then a MAPE testing is ap- plied with results are shown in Table 7. It is realized that the MAPE has a value of 7.66%. This value is no- ticeable as it is much lower than usual MAPE errors for long-term forecasting14. Therefore, if there is no significant fluctuation in economics and society, then the equation (5) could be a feasible forecasting tool for national power system. Forecast on electricity consumptionof indus- try and construction sectors to 2030 Forecasted value of GDP of industry and construc- tion sectors, proportion of industry and construction sectors in entire economics, and income per capita of Vietnam to 2030 are collected and listed in Ta- ble 89. As mentioned processing, those data will be converted into its corresponding natural logarithms. Then the equation (5) is applied to forecast the elec- tricity consumption of Vietnam’s industry and con- struction sectors in the future. Forecasted results are shown in Table 9. The results show that: (1) in the low scenario, the electricity demand of the industry and construction sectors will reach 111,039 GWh, 167,945 GWh, and 227,634 GWh in 2020, 2025, and 2030, respectively; (2) the forecasted values of the base scenario are 112,853 GWh in 2020, 172,691 GWh in 2025, and 242,027 GWh in 2030; and (3) the corresponding pre- dictions for high scenario in 2020, 2025, and 2030 are 115,947 GWh, 181,591 GWh, and 257,272 GWh, re- spectively. The vaules in high scenario in this study 319 Science & Technology Development Journal – Engineering and Technology, 3(1):316-325 Table 1: Units for Magnetic Properties9–11 Year Electricity con- sumption GDP of the sec- tor Proportion consumption of the sector Income Electric price GWh Billion US$ % US$/ person VNĐ/ kWh 1990 2,845 1,467.4 46.0 130 640.0 1991 3,080 2,287.4 46.8 110 640.0 1992 3,197 2,690.1 46.1 130 640.0 1993 3,477 3,809.3 44.4 170 645.0 1994 3,944 4,701.6 42.5 200 645.0 1995 4,614 5,962.9 41.3 260 651.0 1996 5,503 7,330.7 41.1 310 667.0 1997 6,163 8,610.1 40.3 350 673.0 1998 6,781 8,840.7 38.4 360 684.0 1999 7,590 9,894.4 38.7 370 700.0 2000 8,743 11,504.3 41.6 400 718.0 2001 10,503 12,514.3 40.6 430 742.0 2002 12,681 13,547.1 41.9 460 752.0 2003 15,290 15,665.7 43.8 510 761.0 2004 17,896 18,508.8 45.1 590 774.0 2005 21,302 21,976.2 46.7 680 783.0 2006 24,290 25,609.3 47.4 760 789.0 2007 29,212 29,813.6 50.0 850 842.0 2008 34,156 36,755.2 50.7 1,000 948.5 2009 38,504 39,637.0 50.6 1,120 970.9 2010 44,428 37,251.1 51.9 1,270 1,058.0 2011 50,085 43,703.9 52.9 1,390 1,242.0 2012 55,300 52,289.8 52.4 1,550 1,437.0 2013 60,773 56,828.7 52.7 1,740 1,508.8 2014 69,185 61,846.7 53.9 1,900 1,508.8 2015 76,795 64,252.8 52.6 1,990 1,622.0 320 Science & Technology Development Journal – Engineering and Technology, 3(1):316-325 Table 2: Converted results into natural logarithms ln(ECt ) ln(Gt ) ln(Xt ) ln(It ) ln(Pt ) 1990 7.95 7.29 3.83 4.87 6.46 1991 8.03 7.74 3.85 4.70 6.46 1992 8.07 7.90 3.83 4.87 6.46 1993 8.15 8.25 3.79 5.14 6.47 1994 8.28 8.46 3.75 5.30 6.47 1995 8.44 8.69 3.72 5.56 6.48 1996 8.61 8.90 3.72 5.74 6.50 1997 8.73 9.06 3.70 5.86 6.51 1998 8.82 9.09 3.65 5.89 6.53 1999 8.93 9.20 3.66 5.91 6.55 2000 9.08 9.35 3.73 5.99 6.58 2001 9.26 9.43 3.70 6.06 6.61 2002 9.45 9.51 3.74 6.13 6.62 2003 9.63 9.66 3.78 6.23 6.63 2004 9.79 9.83 3.81 6.38 6.65 2005 9.97 10.00 3.84 6.52 6.66 2006 10.10 10.15 3.86 6.63 6.67 2007 10.28 10.30 3.91 6.75 6.74 2008 10.44 10.51 3.93 6.91 6.85 2009 10.56 10.59 3.92 7.02 6.88 2010 10.70 10.53 3.95 7.15 6.96 2011 10.82 10.69 3.97 7.24 7.12 2012 10.92 10.86 3.96 7.35 7.27 2013 11.01 10.95 3.96 7.46 7.32 2014 11.14 11.03 3.99 7.55 7.32 2015 11.25 11.07 3.96 7.60 7.39 Table 3: Stationary test of forecasting equation Variable ADF Test P-P Test T. statistic Prob. value T. statistic Prob. Value lnECt -0.556033 0.8630 0.419163 0.9797 lnGt -4.154082 0.0037 -4.154082 0.0037 lnIt -2.815066 0.0711 -0.475247 0.8805 lnXt -2.271965 0.1893 -0.395858 0.8955 lnPt 2.593966 1 2.324744 0.9999 321 Science & Technology Development Journal – Engineering and Technology, 3(1):316-325 Table 4: Co-Integration test Hypothesized Trace Statistic Critical Value Prob. None * 107.4064 69.81889 0.0000 At most 1 * 68.64893 47.85613 0.0002 At most 2 * 37.34043 29.79707 0.0056 At most 3 * 15.94579 15.49471 0.0427 At most 4 2.010153 3.841466 0.1562 Table 5: Results of the first calculation Variable Coefficient Std. Error t-Statistic Prob. C -4.798612 1.197327 -4.007772 0.0006 lnGt 0.339614 0.165795 2.048397 0.0500 lnIt 0.686921 0.241594 2.843290 0.0097 lnPt -0.093344 0.218511 -0.427181 0.6736 lnXt 1.941250 0.328647 5.906802 0.0000 R2 0.992581 Table 6: Results of the second calculation Variable Coefficient Std. Error t-Statistic Prob. C -5.050521 1.022495 -4.939411 0.0001 lnGt 0.372298 0.144326 2.579560 0.0171 lnIt 0.623799 0.187552 3.326022 0.0031 lnXt 1.864202 0.269579 6.915226 0.0000 R2 0.992516 are less than from 9.0% to 15.8 % of that of the based of in the Revised version of master plan N0. VII 9. CONCLUSION &DICUSSION In this paper, an econometric model namely Cobb Douglas production function has been applied to forecast the electricity consumption of Vietnam’s in- dustry and construction sectors to 2030. Four vari- ables have been identified as: (1) GDP of indus- try and construction sectors; (2) income per person; (3) proportion of the industry and construction sec- tors in entire economics (GDP); and (4) electricity price. Three testing methods have been launched to build the most reliable and highest accuracy predic- tion equation, they are: (1) stationary testing; (2) co- integration testing; and (3) R2 and p-value testing. The equation (5) has the MAPE has a value of 7.66%. This value is a very good MAPE error for long-term forecasting. There are three qualified variables (GDP of the industry and construction sectors, proportion of industry and construction sectors in entire eco- nomics and income per person) have been figured- out. It means that the electricity price has no impacts on the electricity consumption behaviors of Vietnam’s industry and construction sectors. It can be explained that Vietnam’s electricity tariff has been fixed based on national policy. Additionally, there is a num- ber of manufacturing industries is currently provided with special subsidy-tariff policies. The demand of Vietnam’s the industry and construction sectors in 2030 will be doubled in comparison with the values of 2020 and tripled to the consumption of 2016 (85,305 GWh). For this reason, it is an urgent issue to release an appropriate investment on national power system. Moreover, variable of electric price does not effect to the demand. This is a very critical result of the study for authority governors in Vietnam. There is very common that forecating of MOIT (PDP.VII rev.) are always higher than real one. 322 Science & Technology Development Journal – Engineering and Technology, 3(1):316-325 Table 7: Results of themape testing for the forecasting equation (5) Year Consumption by euqation (5) Historical data of con- sumption Error Unit GWh GWh % 1990 2,530.755 2,845 11,0 1991 2,777.509 3,080 9,8 1992 3,183.654 3,197 0,4 1993 3,994.011 3,477 -14,9 1994 4,406.035 3,944 -11,7 1995 5,374.738 4,614 -16,5 1996 6,418.859 5,503 -16,6 1997 7,086.356 6,163 -15,0 1998 6,656.287 6,781 1,8 1999 7,163.889 7,590 5,6 2000 9,101.820 8,743 -4,1 2001 9,389.121 10,503 10,6 2002 10,696.150 12,681 15,7 2003 13,078.730 15,290 14,5 2004 16,094.380 17,896 10,1 2005 20,003.750 21,302 6,1 2006 23,335.400 24,290 3,9 2007 29,250.450 29,212 -0,1 2008 35,912.550 34,156 -5,1 2009 39,496.170 38,504 -2,6 2010 43,764.510 44,428 1,5 2011 50,915.380 50,085 -1,7 2012 57,235.370 55,300 -3,5 2013 64,131.260 60,773 -5,5 2014 72,913.230 69,185 -5,4 2015 72,736.170 76,795 5,3 MAPE 7.66 ABBREVIATION GDP: Gross Domestic Production EVN: Vietnam Electricity MOIT: Ministry of Industry and Trade PDP.VII rev.: Revised version of Master Plan N0. VII for Power System in Vietnam MAPE: Mean Absolute Percentage Error ANN: Artificial Neural Networks ADF: Augmented Dickey Fuller P-P: Phillips-Perron CONFLICT OF INTEREST Group of authors have no conflict on interest in pub- lishing of the paper. 323 Science & Technology Development Journal – Engineering and Technology, 3(1):316-325 Table 8: Forecasted values of the proportion in GDP of the sectors in total, and income per person to 2030 9 Variable Scenario 2020 2025 2030 Income per person [US$/yr] Low Scenario 3,307 4,939 7,205 Base Scenario 3,370 5,111 7,836 High Scenario 3,485 5,450 8,450 Proportion of the sectors [%] 55.48 60.35 62.39 GDP of the sectors [%] Low Scenario 65,428 66,603 67,779 Base Scenario 66,212 67,779 69,424 High Scenario 67,309 69,659 72,089 Table 9: Forecast on the electricity consumption of the sectors to 2030 [GWh] 2020 2025 2030 Low Scenario 111,039 167,945 227,634 Base Scenario 112,853 172,691 242,027 High Scenario 115,947 181,591 257,272 Revised version of master plan n0. VII 9 Base Scenario 127,380 206,446 305,712 AUTHOR CONTRIBUTION Viet-Cuong VO, who deliver ideal, method, and data of the study. Correcting the manuscript. Hoang-Phuong NGUYEN, who running the pro- gram. Le-Duy-Luan NGUYEN, who draft the manuscript. Van-Hung PHAM, who do the first try of the pro- gram. REFERENCES 1. Vietnam Electricity (EVN), Vietnam Power 2015 Report. 2015;. 2. Ozoh P, Abd-Rahman S, Labadin J, Apperley M. A Compar- ative Analysis of Techniques for Forecasting Electricity Con- sumption. International Journal of Computer Applications. 2014;88(15). Available from: https://doi.org/10.5120/15426- 3841. 3. Husain S, Islam MS. A Test for the Cobb Douglas Production Function in Manufacturing Sector: The Case of Bangladesh. International Journal of Business and Economics Research. 2016;5(5):149–154. Available from: https://doi.org/10.11648/ j.ijber.20160505.13. 4. Imtiaz AK, Norman BM, AmranMR, SaleemM,WahabNIA,Mo- hibullah. Evaluation and Forecasting of Long Term Electricity Consumption Demand for Malaysia by Analysis. The First In- ternational Power and Energy Conference PECon 2006, Putra- jaya, Malaysia. 2006;Available from: https://doi.org/10.1109/ PECON.2006.346658. 5. Muhammad S, Hooi HL. The dynamics of electricity consump- tion and economic growth: A revisit study of their causal- ity in Pakistan. Energy. 2012;39:146–153. Available from: https://doi.org/10.1016/j.energy.2012.01.048. 6. Jiahai Y, Changhong Z, Shunkun Y, Zhaoguang H. Electric- ity consumption an economic growth in China: Cointegration and co-feature analysis. Energy Economics. 2007;29:1179– 1191. Available from: https://doi.org/10.1016/j.eneco.2006.09. 005. 7. Nguyen VMH, Nguyen KTP, Vo CV, Phan BTT. Forecast on 2030 Vietnam Electricity Consumption. Engineering, Technology & Applied Science Research. 2018;8(3):2869–2874. Available from: https://doi.org/10.1109/ICELTICS.2017.8253238PMCid: PMC5341203. 8. Robert FE, Granger CWJ. Co-integration and Error Correc- tion: Representation, Estimation, and Testing. Econometrica. 1987;55(2):251–276. Available from: https://doi.org/10.2307/ 1913236. 9. Institute of Energy, MOIT, ”Revised version of master plan N0. VII for power system in Vietnam”. 2016;. 10. General Statistics Office of Vietnam, ”Master investigation on populayion and households 2014 - Vietnam”. 2014;. 11. Data world bank;Available from: country/vietnam. 12. Green SB. How many subjects does it take to do a regres- sion analysis? Multivariate Behavioral Research. 1991;26:499– 510. PMID: 26776715. Available from: https://doi.org/10.1207/ s15327906mbr2603_7. 13. Barbara GT, Linda SF. Using multivariate statistics 5th Edn. California State University, Northridge, Pearson Education Inc. 2007;. 14. Amstrong, Scott J. Long-Range Forecasting: From Crystal Ball to Computer. Wiley-Interscience; Second edition. 1985;. 324 Tạp chí Phát triển Khoa học và Công nghệ – Engineering and Technology, 3(1):316-325 Open Access Full Text Article Bài Nghiên cứu 1Trường Đại học Sư phạm Kỹ thuật Tp. HCM, Số 1, Võ Văn Ngân, QuậnThủ Đức, Tp. HCM, Việt Nam 2Trường Đại học Tiền Giang, Số 119, Ấp Bắc, Phường 5, Tp. MỹTho, Tỉnh Tiền Giang, Việt Nam 3Trường Đại học Kiến trúc Tp. HCM, Số 196, Pasteur, Quận 3, Tp. HCM, Việt Nam Liên hệ Võ Viết Cường, Trường Đại học Sư phạm Kỹ thuật Tp. HCM, Số 1, Võ Văn Ngân, Quận Thủ Đức, Tp. HCM, Việt Nam Email: cuongvv@hcmute.edu.vn Lịch sử  Ngày nhận: 15-12-2019  Ngày chấp nhận: 13-3-2020  Ngày đăng: 31-3-2020 DOI : 10.32508/stdjet.v3i1.646 Bản quyền © ĐHQG Tp.HCM. Đây là bài báo công bố mở được phát hành theo các điều khoản của the Creative Commons Attribution 4.0 International license. Mô hình kinh tế lượng dự báo nhu cầu điện năng của ngành công nghiệp và xây dựng tại Việt Nam đến 2030 Võ Viết Cường1,*, Nguyễn Hoàng Phương1,2, Nguyễn Lê Duy Luân3, Phạm Văn Hùng1 Use your smartphone to scan this QR code and download this article TÓM TẮT Dự báo chính xác nhu cầu điện dài hạn có vai trò rất quan trọng cho quy hoạch hệ thống điện tại bất kỳ quốc gia nào. Việt Nam là một trong những nước có kinh tế phát triển nhanh nhất thế giới, nhu cầu điện tăng trung bình khoảng 15%/năm trong suốt ba thập kỷ vừa qua. Sự đóng góp của ngành công nghiệp và ngành xây dựng trong GDP tăng đều qua các năm, và hiện đang giữ tỷ trọng lớn nhất trong tiêu thụ điện với tỷ lệ trên 50% phụ tải quốc gia. Làm sao để xác dịnh chính xác tiêu thụ điện của những lĩnh vực trên góp phần quan trọng cho việc quy hoạch hệ thống điện. Bài báo này đề xuất áp dụngmô hình kinh tế lượng với hàm sản xuất Cobb Douglas để dự báo nhu cầu điện của ngành công nghiệp và ngành xây dựng Việt Nam tới năm 2030. Bốn biến được sử dụng là giá trị trong GDP của hai ngành trên, thu nhập đầu người, tỷ trọng điện năng tiêu thụ của 2 ngành trong tổng nhu cầu của nền kinh tế, và giá điện. Kết quả dự báo cho thấy phương pháp đề xuất có sai số MAPE ở mức khá thấp là 7,66%. Biến giá điện không tác động đến nhu cầu điện của 2 ngành. Đây là kết quả có giá trị của nghiên cứu cho các nhà quả lý tại Việt Nam. Nhu cầu điện theo kịch bản cơ sở của GDP và thu nhập đầu người được dư báo lần lượt là 112.853 GWh, 172.691 GWh, và 242.027 GWh tương ứng với các năm 2020, 20205, và 2030. Với kịch bản cao, nhu cầu tương ứng là 115.947 GWh, 181.591 GWh, và 257.272 GWh. Những giá trị của kịch bản cao thấp hơn từ 9,0% đến 15,8% so với giá trị tương ứng trong kịch bản cơ sở của Tồng Sơ đồ 7 hiệu chỉnh. Từ khoá: Dự báo, Mô hình kinh tế lượng, Nhu cầu điện, Hàm sản xuất Cobb Douglas, Ngành công nghiệp & xây dựng Trích dẫn bài báo này: Viết Cường V, Hoàng Phương N, Lê Duy Luân N, Văn Hùng P. Mô hình kinh tế lượng dự báo nhu cầu điện năng của ngành công nghiệp và xây dựng tại Việt Nam đến 2030. Sci. Tech. Dev. J. - Eng. Tech.; 3(1):316-325. 325