A seasonal hybrid procedure for electricity demand forecasting in China

Suling Zhu, Jianzhou Wang*, Weigang Zhao, Jujie Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

86 Citations (Scopus)

Abstract

Electricity is a special energy which is hard to store, so the electricity demand forecasting in China remains an important problem. This paper aims at developing an improved hybrid model for electricity demand in China, which takes the advantages of moving average procedure, combined method, hybrid model and adaptive particle swarm optimization algorithm, known as MA-C-WH. It is designed for making trend and seasonal adjustments which simultaneously presents the electricity demand forecasts. Four actual electricity demand time series in China power grids are selected to illustrate the proposed MA-C-WH model, and one existing seasonal autoregressive integrated moving average model (SARIMA) is selected to compare with the proposed model using the same data series. The results of popular forecasting precision indexes show that our proposed model is an effective forecasting technique for seasonal time series with nonlinear trend.

Original languageEnglish
Pages (from-to)3807-3815
Number of pages9
JournalApplied Energy
Volume88
Issue number11
DOIs
Publication statusPublished - Nov 2011
Externally publishedYes

Keywords

  • Electricity demand
  • Forecasting
  • Optimization

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