Pareto Optimal Prediction Intervals of Electricity Price

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 re...

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Bibliographic Details
Published inIEEE transactions on power systems Vol. 32; no. 1; pp. 817 - 819
Main Authors Wan, Can, Niu, Ming, Song, Yonghua, Xu, Zhao
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial neural networks
Electricity price
Electricity pricing
Electricity supply industry
extreme learning machine
Forecasting
Genetic algorithms
Intervals
Machine learning
NSGA-II
Pareto optimization
Pareto optimum
Power system reliability
prediction intervals
Probabilistic logic
Reliability
Sorting algorithms
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Summary: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.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2016.2550867