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Analytical Iterative Multistep Interval Forecasts of Wind Generation Based on TLGP
Journal article   Open access   Peer reviewed

Analytical Iterative Multistep Interval Forecasts of Wind Generation Based on TLGP

Juan Yan, Kang Li, Erwei Bai, Xiaodong Zhao, Yusheng Xue and Aoife M Foley
IEEE transactions on sustainable energy, Vol.10(2), pp.625-636
04/2019
DOI: 10.1109/TSTE.2018.2841938
url
https://doi.org/10.1109/TSTE.2018.2841938View
Published (Version of record) Open Access

Abstract

Probabilistic wind power forecasting has become an important tool for optimal economic dispatch and unit commitment of modern power systems with significant renewable energy penetrations. Ensemble forecasting based on Monte Carlo simulation has been widely adopted by grid operators, but other probabilistic approaches, such as multistep iterative wind power forecasting have not yet been fully explored. The associated uncertainty analysis is an important yet challenging issue in this area. This paper proposes to use an analytic interval forecasting framework to estimate the forecasting uncertainty and its propagation with multisteps for two wind farms based on the temporally local Gaussian process (TLGP) model. The key findings confirm that TLGP forecasting not only has better accuracy but is also more reliable and sharp than other benchmark models. This paper provides an innovative analytical framework for iterative multistep interval forecasts.
 Forecasting gaussian process Gaussian processes Predictive models Probabilistic forecasting Probabilistic logic Uncertainty uncertainty propogation wind energy Wind forecasting Wind power generation

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