Accurate Short-Term Power Forecasting of Wind Turbines: The Case of JejuIsland's Wind Farm

Collection with item attached
2017
Item details URL
http://open-repository.kisti.re.kr/cube/handle/open_repository/474198.do
DOI
10.3390/en10060812
Title
Accurate Short-Term Power Forecasting of Wind Turbines: The Case of JejuIsland's Wind Farm
Description
This work was supported by the Korea Institute of Energy TechnologyEvaluation and Planning (KETEP) and the Ministry of Trade, Industry &Energy (MOTIE) of the Republic of Korea (No. 20161210200560).
abstract
Short-term wind power forecasting is a technique which tells system operators how much wind power can be expected at a specific time. Due to the increasing penetration of wind generating resources into the power grids, short-term wind power forecasting is becoming an important issue for grid integration analysis. The high reliability of wind power forecasting can contribute to the successful integration of wind generating resources into the power grids. To guarantee the reliability of forecasting, power curves need to be analyzed and a forecasting method used that compensates for the variability of wind power outputs. In this paper, we analyzed the reliability of power curves at each wind speed using logistic regression. To reduce wind power forecasting errors, we proposed a short-term wind power forecasting method using support vector machine (SVM) based on linear regression. Support vector machine is a type of supervised leaning and is used to recognize patterns and analyze data. The proposed method was verified by empirical data collected from a wind turbine located on Jeju Island.
provenance
Made available in Cube on 2018-09-28T10:39:46Z (GMT). No. of bitstreams: 0
language
English
author
Park, BeomJun
Hur, Jin
orcid
Hur, Jin/0000-0003-2239-3602
accessioned
2018-09-28T10:39:46Z
available
2018-09-28T10:39:46Z
issued
2017
citation
ENERGIES(10): 6
issn
1996-1073
uri
http://open-repository.kisti.re.kr/cube/handle/open_repository/474198.do
Funder
산업통상자원부
Funding Program
스마트그리드핵심기술개발
Project ID
1415153973
Jurisdiction
Rep.of Korea
Project Name
Electric Vehicle Charging Demand Monitoring, Forecasting, and Distributing Technology Development for Anal
rights
openAccess
subject
wind power forecasting
enhancing reliability
power curve
supportvector machine (SVM)
support vector regression (SVR)
type
article


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