Literature DB >> 28039623

Forecasting residential electricity demand in provincial China.

Hua Liao1,2,3, Yanan Liu4,5,6, Yixuan Gao4,5,6,7, Yu Hao4,5,6, Xiao-Wei Ma4,5,6, Kan Wang4,5,6.   

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.

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Keywords:  China; Electricity demand; Forecast; Piecewise linear model; Provincial panel data; Residential sector

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Year:  2016        PMID: 28039623     DOI: 10.1007/s11356-016-8275-8

Source DB:  PubMed          Journal:  Environ Sci Pollut Res Int        ISSN: 0944-1344            Impact factor:   4.223


  1 in total

Review 1.  The effects of China's universal two-child policy.

Authors:  Yi Zeng; Therese Hesketh
Journal:  Lancet       Date:  2016-10-15       Impact factor: 79.321

  1 in total
  1 in total

1.  Developing a Mixed Neural Network Approach to Forecast the Residential Electricity Consumption Based on Sensor Recorded Data.

Authors:  Simona-Vasilica Oprea; Alexandru Pîrjan; George Căruțașu; Dana-Mihaela Petroșanu; Adela Bâra; Justina-Lavinia Stănică; Cristina Coculescu
Journal:  Sensors (Basel)       Date:  2018-05-05       Impact factor: 3.576

  1 in total

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