title: Forecasting residential electricity demand in provincial China.
abstract: 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.
authors.affiliation: School of Management and Economics, Beijing Institute of Technology, 5 Zhongguancun South Street, Haidian District, Beijing, 100081, China. hliao@bit.edu.cn.|Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology (BIT), Beijing, 100081, China. hliao@bit.edu.cn.|Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing, 100081, China. hliao@bit.edu.cn.
authors.forename: Hua
authors.initials: H
authors.lastname: Liao
authors.affiliation: School of Management and Economics, Beijing Institute of Technology, 5 Zhongguancun South Street, Haidian District, Beijing, 100081, China.|Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology (BIT), Beijing, 100081, China.|Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing, 100081, China.
authors.forename: Yanan
authors.initials: Y
authors.lastname: Liu
authors.affiliation: School of Management and Economics, Beijing Institute of Technology, 5 Zhongguancun South Street, Haidian District, Beijing, 100081, China.|Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology (BIT), Beijing, 100081, China.|Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing, 100081, China.|University of New Hampshire, 56 College Rd, Durham, NH, 03824, USA.
authors.forename: Yixuan
authors.initials: Y
authors.lastname: Gao
authors.affiliation: School of Management and Economics, Beijing Institute of Technology, 5 Zhongguancun South Street, Haidian District, Beijing, 100081, China.|Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology (BIT), Beijing, 100081, China.|Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing, 100081, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Hao
authors.affiliation: School of Management and Economics, Beijing Institute of Technology, 5 Zhongguancun South Street, Haidian District, Beijing, 100081, China.|Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology (BIT), Beijing, 100081, China.|Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing, 100081, China.
authors.forename: Xiao-Wei
authors.initials: XW
authors.lastname: Ma
authors.affiliation: School of Management and Economics, Beijing Institute of Technology, 5 Zhongguancun South Street, Haidian District, Beijing, 100081, China.|Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology (BIT), Beijing, 100081, China.|Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing, 100081, China.
authors.forename: Kan
authors.initials: K
authors.lastname: Wang
mesh_terms: D002681:China; D003031:Coal; D003246:Conservation of Energy Resources / Q000639:trends*; D003627:Data Interpretation, Statistical; D003906:Developing Countries; D004560:Electricity; D005544:Forecasting; D006801:Humans; D012424:Rural Population; D014505:Urban Population
chemical_list: D003031:Coal
keywords: China; Electricity demand; Forecast; Piecewise linear model; Provincial panel data; Residential sector