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Energies 2017, 10(6), 812; doi:10.3390/en10060812

Accurate Short-Term Power Forecasting of Wind Turbines: The Case of Jeju Island’s Wind Farm

Department of Electrical Engineering, Sangmyung University, Seoul 03016, Korea
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Academic Editor: Marco Mussetta
Received: 22 April 2017 / Revised: 7 June 2017 / Accepted: 13 June 2017 / Published: 15 June 2017
(This article belongs to the Special Issue Wind Generators Modelling and Control)
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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. View Full-Text
Keywords: wind power forecasting; enhancing reliability; power curve; support vector machine (SVM); support vector regression (SVR) wind power forecasting; enhancing reliability; power curve; support vector machine (SVM); support vector regression (SVR)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Park, B.; Hur, J. Accurate Short-Term Power Forecasting of Wind Turbines: The Case of Jeju Island’s Wind Farm. Energies 2017, 10, 812.

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