Forecasting residential electricity demand in provincial China

In China, more than 80% electricity comes from coal which dominates the CO2 emissions. Residential electricity demand forecasting plays a significant role in electricity infrastructure planning and energy policy designing, but it is challenging to make an accurate forecast for developing countries. This paper forecasts the provincial residential electricity consumption of China in the 13th Five-Year-Plan (2016–2020) period using panel data. To overcome the limitations of widely used predication models with unreliably prior knowledge on function forms, a robust piecewise linear model in reduced form is utilized to capture the non-deterministic relationship between income and residential electricity consumption. The forecast results suggest that the growth rates of developed provinces will slow down, while the less developed will be still in fast growing. The national residential electricity demand will increase at 6.6% annually during 2016–2020, and populous provinces such as Guangdong will be the main contributors to the increments.

[1]  Hyojoo Son,et al.  Short-term forecasting of electricity demand for the residential sector using weather and social variables , 2017 .

[2]  Christian von Hirschhausen,et al.  Long-term electricity demand in China — From quantitative to qualitative growth? , 2000 .

[3]  Boqiang Lin Electricity demand in the People's Republic of China : investment requirement and environmental impact , 2003 .

[4]  Hui Zhou,et al.  Long- and short-run elasticities of residential electricity consumption in China: a partial adjustment model with panel data , 2016 .

[5]  Yi Zeng,et al.  The effects of China's universal two-child policy , 2016, The Lancet.

[6]  J. Razmi,et al.  Forecasting electricity consumption by clustering data in order to decline the periodic variable’s affects and simplification the pattern , 2009 .

[7]  M. Auffhammer,et al.  Forecasting The Path of U.S. CO2 Emissions Using State-Level Information , 2010, Review of Economics and Statistics.

[8]  Theologos Dergiades,et al.  Estimating Residential Demand for Electricity in the United States, 1965-2006 , 2008 .

[9]  M. Auffhammer,et al.  Forecasting the Path of China's CO2 Emissions Using Province Level Information , 2007 .

[10]  M. G. Morgan,et al.  Bounding US electricity demand in 2050 , 2016 .

[11]  Massimo Filippini,et al.  Residential electricity demand in Spain: New empirical evidence using aggregate data , 2013 .

[12]  Pat Bodger,et al.  A comparison of Logistic and Harvey models for electricity consumption in New Zealand , 2005 .

[13]  M. Nerlove,et al.  Biases in dynamic models with fixed effects , 1988 .

[14]  Frederick L. Joutz,et al.  Residential electricity demand in Taiwan , 2004 .

[15]  Timothy G. Conley GMM estimation with cross sectional dependence , 1999 .

[16]  G. Hondroyiannis Estimating residential demand for electricity in Greece , 2004 .

[17]  E. Tserkezos Forecasting residential electricity consumption in Greece using monthly and quarterly data , 1992 .

[18]  Paul A. Steenhof,et al.  Factors affecting electricity generation in China: Current situation and prospects , 2007 .

[19]  Yochanan Shachmurove,et al.  Modeling and forecasting energy consumption in China: Implications for Chinese energy demand and imports in 2020 , 2008 .

[20]  Lambros Ekonomou,et al.  Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models , 2008 .

[21]  J. Musango Household electricity access and consumption behaviour in an urban environment: The case of Gauteng in South Africa , 2014 .

[22]  Seung-Hoon Yoo,et al.  Electricity consumption and economic growth: A cross-country analysis , 2010 .

[23]  Ming Yang,et al.  China’s rural electricity market—a quantitative analysis , 2004, Energy.

[24]  Georges A. Darbellay,et al.  Forecasting the short-term demand for electricity: Do neural networks stand a better chance? , 2000 .

[25]  Hua Liao,et al.  How does carbon dioxide emission change with the economic development? Statistical experiences from 132 countries , 2013 .

[26]  Anna Alberini,et al.  Residential Consumption of Gas and Electricity in the U.S.: The Role of Prices and Income , 2011 .

[27]  M. Arellano,et al.  Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations , 1991 .

[28]  Richard Schmalensee,et al.  World energy consumption and carbon dioxide emissions : 1950-2050 , 1995 .

[29]  Yi-Ming Wei,et al.  Why did the historical energy forecasting succeed or fail? A case study on IEA's projection , 2016 .

[30]  Clemens Fuest,et al.  Why is there Corporate Taxation? The Role of Limited Liability Revisited , 2007 .