Yan, J, Li, K orcid.org/0000-0001-6657-0522, Bai, E et al. (3 more authors) (2019) Analytical Iterative Multi-Step Interval Forecasts of Wind Generation based on TLGP. IEEE Transactions on Sustainable Energy, 10 (2). pp. 625-636. ISSN 1949-3029
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 is commonly used by many grid operators, but other probabilistic approaches, such as multi-step 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 of a wind farm in Ireland based on the Temporally Local Gaussian Process (TLGP) model and evaluates the probabilistic forecasting metrics of reliability and sharpness. The key findings confirm that TLGP not only has better forecasting accuracy but is also less sensitive to uncertainty propagation than Gaussian Process (GP). This work provides an effective analytical framework for iterative multi-step interval forecasting.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | (c) 2018, IEEE. This is an author produced version of a paper published in IEEE Transactions on Sustainable Energy. 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: | Forecasting, Wind power generation, Wind forecasting, Uncertainty, Probabilistic logic, Gaussian processes, Predictive models; probabilistic forecasting, Gaussian process, uncertainty propogation, wind energy. |
Dates: |
|
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: | 05 Jul 2018 10:09 |
Last Modified: | 29 May 2019 00:43 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TSTE.2018.2841938 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:132836 |