Quantitative modelling of electricity consumption using computational intelligence aided design

Yi Chen, Guangfeng Zhang, Tongdan Jin, Shaomin Wu, Bei Peng

Research output: Contribution to journalArticle

Abstract

High electricity consumption is of concern to the world for a variety of reasons, including its social-economic-environmental coupled impacts on well-being of individuals, social life and the federal energy policies. This paper proposes a quantitative model to examine the long-term relationship between annual electricity consumption and its major macroeconomic variables, including gross domestic product, electricity price, efficiency, economic structure, and carbon dioxide emission, using computational intelligence aided design (CIAD). It develops a firefly algorithm with variable population (FAVP) to obtain the parameters of the electricity consumption model through optimising two proposed trend indices: moving mean of the average precision (mmAP) and moving mean of standard derivation (mmSTD).
Original languageEnglish
Pages (from-to)143–152
Number of pages10
JournalJournal of Cleaner Production
Volume69
DOIs
Publication statusPublished - 2014

Keywords

  • computer modelling
  • electricity
  • CIAD
  • algorithm

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