Pareto Optimal Prediction Intervals of Electricity Price

Can Wan, Ming Niu, Yonghua Song, Zhao Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

42 Citations (Scopus)

Abstract

This letter proposes a novel Pareto optimal prediction interval construction approach for electricity price combing extreme learning machine and non-dominated sorting genetic algorithm II (NSGA-II). The Pareto optimal prediction intervals are produced with respect to the formulated two objectives reliability and sharpness. The effectiveness of proposed approach has been verified through the numerical studies on Australia electricity market data.
Original languageEnglish
Article number7448478
Pages (from-to)817-819
Number of pages3
JournalIEEE Transactions on Power Systems
Volume32
Issue number1
DOIs
Publication statusPublished - 1 Jan 2017

Keywords

  • Electricity price
  • extreme learning machine
  • NSGA-II
  • prediction intervals

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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