Yan, J, Li, K orcid.org/0000-0001-6657-0522, Bai, E-W et al. (2 more authors) (2016) Hybrid Probabilistic Wind Power Forecasting Using Temporally Local Gaussian Process. IEEE Transactions on Sustainable Energy, 7 (1). pp. 87-95. ISSN 1949-3029
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Error analysis, forecasting, Gaussian process (GP), wind power |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Feb 2019 15:22 |
Last Modified: | 25 Feb 2019 17:43 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TSTE.2015.2472963 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:142979 |