An Adaptive Bilevel Programming Model for Nonparametric Prediction Intervals of Wind Power Generation

It is hard to obtain precise wind power forecasting due to the chaotic nature of weather systems. Prediction intervals become an efficient tool to quantify the uncertainty involved in wind power forecasting. Traditional central prediction intervals are widely produced by forecasters, however, which...

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Veröffentlicht in:	IEEE transactions on power systems Jg. 35; H. 1; S. 424 - 439
Hauptverfasser:	Zhao, Changfei, Wan, Can, Song, Yonghua
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Algorithms Artificial neural networks bilevel programming Convergence Electric power generation Forecasting Intervals Mathematical models Nonlinear programming Nonparametric statistics Prediction interval Predictive models Probabilistic logic Programming quantile regression spatial branch-and-bound Statistical analysis Uncertainty Weather forecasting Wind power Wind power generation
ISSN:	0885-8950, 1558-0679
Online-Zugang:	Volltext
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