Traditional method of forecasting electricity consumption based only on GDP was sometimes ineffective. In this paper, urbanisation rate (UR) was introduced as an additional predictor to improve the electricity demand forecast in China at provincial scale, which was previously based only on GDP. Historical data of Shaanxi province from 2000 to 2013 was collected and used as case study. Four regression models were proposed and GDP, UR, and electricity consumption (EC) were used to establish the parameters in each model. The model with least average error of hypothetical forecast results in the latest three years was selected as the optimal forecast model. This optimal model divides total EC into four parts, of which forecasts can be made separately. It was found that GDP was only better correlated than UR on household EC, whilst UR was better on the three sectors of industries. It was concluded that UR is a valid predictor to forecast electricity demand at provincial level in China nowadays. Being provided the planned value of GDP and UR from the government, EC in 2015 were forecasted as 131.3 GWh.
The consumption of electricity is largely related to the economic development, industrialisation, and urbanisation process in China for the past two decades. It is crucial for the local or provincial governments as well as electricity providers to forecast the demand of electricity in order to achieve better installation planning and administration. A common practical method widely applied by most of the electric power companies is using GDP planned by either central or local authorities as the predictor (or in other transforms, e.g., GDP growth rate in electricity elasticity coefficient method) to estimate the electricity demand for the next few years. The electricity consumption is likewise regarded as part of the economic development index system [
The approach of forecasting electricity based on GDP had been functioning for many years for practical purposes but is being questioned after the appearance of two deviations between the growth rate of electricity consumption and growth rate of GDP in 19971998 and 2008 during the two global financial crises: the elasticity ratio of electricity consumption dropped below 0.6, as shown in Figure
Elasticity ratio of electricity consumption from 1988 to 2012 [
In 1990s, the ways that urbanisation affects energy use in developing countries were discussed by Jones [
There are a few studies addressed on the relationship between urbanisation and total energy consumption [
According to the results of past studies on urbanisation and energy, one could expect that correlation also exists between urbanisation and electricity, a dominating type of modern energy. This relationship since 2000 can be seen from Figure
Electricity consumption and urbanisation rate of China from 2000 to 2013 [
Although the utility industry of China is aware of ineffectiveness of GDP and thus in need of an improved approach to forecast electricity demand, literatures so far have not addressed this issue. Similar issues are present also in the forecasting of other energy sources, such as natural gas. The aim of this paper is to investigate to what extent urbanisation rate can be used as a parameter along with GDP, to provide better forecast on electricity demand in China at provincial scale. Empirical annual data of Shaanxi province was collected and used as a case study. Note also that the planned urbanisation rate for the next year or couple of years, similar to planned GDP, is available from authorities. The results are expected to provide better approaches to forecast electricity demand for power operators and stakeholders.
The historical data of electricity consumption, GDP, and urbanisation rate (worked out from urban population divided by total population) since 2000 was collected from past Shaanxi Statistical Yearbooks (see [
Table
Historical data of electricity consumption, GDP, and urbanisation rate in Shaanxi province from 2000 to 2013.
Year  Electricity consumption (108 Wh)  GDP (CNY in billion)  Urbanisation rate (%)  

Total  1st 
2nd 
3rd 
Household  
2000  292.76  22.90  198.44  38.11  33.31  180.4  32.27 
2001  321.54  25.11  214.87  45.81  35.77  201.1  33.62 
2002  355.97  26.45  237.51  52.12  39.89  225.3  34.63 
2003  393.68  35.84  266.97  56.97  43.91  258.8  35.54 
2004  477.03  28.70  334.60  66.02  47.71  317.6  36.35 
2005  516.00  31.10  358.09  71.46  55.35  393.4  37.24 
2006  580.72  34.39  406.03  78.25  62.06  474.4  39.12 
2007  653.69  36.02  463.61  88.72  65.76  575.7  40.61 
2008  708.02  37.64  497.65  97.33  75.41  731.5  42.09 
2009  740.11  38.09  504.34  108.30  89.38  817.0  43.49 
2010  859.22  39.13  589.51  116.25  112.38  1012.3  45.70 
2011  982.47  42.54  678.49  131.45  129.99  1251.2  47.29 
2012  1066.75  42.65  724.08  151.85  148.16  1445.4  50.02 
2013  1152.22  45.29  773.54  168.69  164.70  1604.5  51.31 
GDP and urbanisation rate (UR) are two major independent variables in this study. The GDP refers to nominal GDP rather than real GDP; that is, any effects of inflation or deflation were ignored. This is mainly because it is the nominal GDP that is often announced by the authorities and thus can be used in forecast. Urbanisation rate is defined as the percentage of the urban population in the total population. Taking practical purposes and convenience into account, other parameters, such as population or electricity price, were not considered because the planned values of them are not available and thus cannot be used for forecasting.
As for the dependent variables, electricity elasticity coefficient is often used to evaluate the responsiveness of electricity consumption to the change of economic variables, for example, income (often in GDP) and price. The range of electricity elasticity coefficient for China varies with different stages of development and therefore was applied mainly in longterm scenarios at the national level as a macro index by governments and policy makers [
Figure
Electricity/GDP elasticity coefficient of Shaanxi province from 2001 to 2013.
Four linear regression models were used to explore the correlations between electricity consumption (EC) and GDP or urbanisation rate (UR):
Model (i): EC = a × GDP + b.
Model (ii): EC = a × UR + b.
Model (iii): EC = a × GDP + b × UR + c.
Model (iv): EC = EC_{1} + EC_{2} + EC_{3} +
In models (i) and (ii), EC is, respectively, considered as a linear function of GDP only and UR only, that is, unary linear regression. In model (iii), EC is thought to be a linear function of both GDP and UR, that is, binary linear regression of GDP and UR. The relative weights of the two variables are a and b respectively. Model (iv) divides EC into four sectors: in primary (EC_{1}), secondary (EC_{2}), tertiary (EC_{3}) industries and household (
All unary linear regressions were performed in IBM SPSS Statistics 22.0. The best fit lines of regression analysis were drawn by minimising the sum of squared residuals. A residual means the difference between an observed value and the fitted value provided by the specific model. Results of the four linear regressions models were summarised below.
Figures
Unary linear regression result of model (i).
Unary linear regression result of model (ii).
Binary linear regression result of model (iii) was shown in Table
Binary linear regression result of model (iii).
Predictor 

Std. error  Beta 

Significance 


a (GDP)  0.1285  0.008  0.22  1.703  0.117  0.9954 
b (UR)  3557.4  589.801  0.78  6.032  0  
c (constant)  −883.63  189.574  N/A  −4.661  0.001 
3D scatter plot and three projection lines among GDP, UR, and EC.
Figure
Linear regression results of four parts of EC in model (iv).
To summarise, the results of regression based on the proposed four models are listed in Table
Summary of regression results for four proposed models.
Model  Regression results of models based on data of Shaanxi (2000–2013) 

Model (i)  EC = 0.0587 × GDP + 258.2674 
Model (ii)  EC = 4549.3198 × UR − 1199.8703 
Model (iii)  EC = 0.1285 × GDP + 3557.4032 × UR − 883.633 
Model (iv)  EC = (105.7723 × UR − 8.3064) + (3089.3848 × UR − 809.9655) 
The effectiveness of the abovementioned four models in forecasting EC at provincial scale cannot be simply judged from their degrees of correlation only. In this section, hypothetical forecasts of EC in the latest three year (i.e., 2011–2013 in this paper) were performed assuming the data prior to that year is yet unknown. For example, hypothetical forecast of EC in 2012 will be worked out by extrapolating the regression model based on data from 2000 to 2011. The model with least average error and sufficient consistency for the latest three years will be selected as the optimal forecast model.
See Table
Errors of hypothetical forecast in 2011–2013 based on prior data.
Model  2011  2012  2013  Average 

(i)  6.87%  6.83%  4.50%  6.07% 
(ii)  5.54%  0.30%  2.21%  2.68% 
(iii)  5.46%  2.20%  0.68%  2.78% 
(iv)  3.89%  1.29%  1.13% 

In addition to model (iv) which has been selected as the optimal model, planned GDP and UR figures are also required to make forecast on EC. These figures at provincial scale are normally announced by the governments or local authorities in their work plan prior to the planned year. For years 2014 and 2015, the planned GDP of Shaanxi province were CNY 1750 and 1920 billion, respectively [
Predicators and forecast results of EC in 2014 and 2015.
Year  Planned GDP  Planned UR  Forecasted EC (108 Wh) 

2014  1750 billion  53.123%  1226.032 
2015  1920 billion  55.000%  1313.479 
In this study, urbanisation rate (UR) was introduced as a predictor to improve the electricity demand forecast based only on GDP in China at provincial scale. Four regression models were proposed and historical data of GDP, UR, and electricity consumption (EC) was used to establish the parameters in each model. The results of regressions based on data from 2000 to 2013 suggest that UR is a valid predictor to forecast EC at provincial scale. The model with least average error of hypothetical forecast results in the latest three years was selected as the optimal forecast model. This model divided total EC into sectors of industries and household parts, of which forecasts can be made separately using the better predictor of either GDP or UR. It was found that GDP was only better correlated than UR on household EC, whilst UR was better on the three sectors of industries. Being provided the planned value of GDP and UR from the government, forecasts of EC in 2014 and 2015 were obtained.
The forecasting process in this paper was aiming to provide an easy and highly adjustable tool for stakeholders to use. No discussion of causality between GDP, UR, and EC was included. The accuracy of forecast result is largely affected by the reliability of the planned figures and thus might be capped. The parameters of forecast model can be adjusted when the new historical data is available. More case studies from other provinces are needed to justify the feasibility of UR as predictor in forecasting EC in China.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The authors thank Zenghu Dang and Lifang Wu for their kind advice in the preparation of this paper.