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Hybrid Probabilistic Wind Power Forecasting Using Temporally Local Gaussian Process
Journal article   Peer reviewed

Hybrid Probabilistic Wind Power Forecasting Using Temporally Local Gaussian Process

Juan Yan, Kang Li, Er-Wei Bai, Jing Deng and Aoife M Foley
IEEE transactions on sustainable energy, Vol.7(1), pp.87-95
01/2016
DOI: 10.1109/TSTE.2015.2472963
url
https://pureadmin.qub.ac.uk/ws/files/16998605/07274766.pdfView
Open Access

Abstract

The demand for sustainable development has resulted in a rapid growth in wind power worldwide. Although various approaches have been proposed to improve the accuracy and to overcome the uncertainties associated with traditional methods, the stochastic and variable nature of wind still remains the most challenging issue in accurately forecasting wind power. This paper presents a hybrid deterministicprobabilistic method where a temporally local moving window technique is used in Gaussian process (GP) to examine estimated forecasting errors. This temporally local GP employs less measurement data with faster and better predictions of wind power from two wind farms, one in the USA and the other in Ireland. Statistical analysis on the results shows that the method can substantially reduce the forecasting error while it is more likely to generate Gaussian-distributed residuals, particularly for short-term forecast horizons due to its capability to handle the time-varying characteristics of wind power.
 Wind Power Analytical models Autoregressive processes Error analysis Forecasting Gaussian process (GP) Predictive models Wind farms Wind forecasting Wind power generation

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