Xue, Y, Yu, C, Li, K orcid.org/0000-0001-6657-0522 et al. (4 more authors) (2016) Adaptive ultra-short-term wind power prediction based on risk assessment. CSEE Journal of Power and Energy Systems, 2 (3). pp. 59-64. ISSN 2096-0042
Abstract
A risk assessment based adaptive ultra-short-term wind power prediction (USTWPP) method is proposed in this paper. In this method, features are first extracted from the historical data, and then each wind power time series (WPTS) is split into several subsets defined by their stationary patterns. A WPTS that does not match any of the stationary patterns is then included in a subset of non-stationary patterns. Each WPTS subset is then related to a USTWPP model that is specially selected and optimized offline based on the proposed risk assessment index. For online applications, the pattern of the last short WPTS is first recognized, and the relevant prediction model is then applied for USTWPP. Experimental results confirm the efficacy of the proposed method.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 CSEE. This is an open access article under the terms of the Creative Commons Non-Commercial No-Derivatives License (CC-BY-NC-ND). https://creativecommons.org/licenses/by-nc-nd/4.0/. |
Keywords: | Error evaluation; offline optimization; online matching; positive error vs negative error; risk index; time series features; wind power prediction |
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: | 26 Nov 2018 10:12 |
Last Modified: | 13 Aug 2019 11:56 |
Status: | Published |
Publisher: | CSEE |
Identification Number: | 10.17775/CSEEJPES.2016.00036 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:139100 |