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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Veröffentlicht in:	IEEE transactions on power systems 2017-01, Vol.32 (1), p.817-819
Hauptverfasser:	Wan, Can, Niu, Ming, Song, Yonghua, Xu, Zhao
Format:	Artikel
Sprache:	eng
Schlagworte:	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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Zusammenfassung:	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