General Information
    • ISSN: 1793-821X
    • Frequency: Quarterly (2013-2014); Bimonthly (Since 2015)
    • DOI: 10.18178/JOCET
    • Editor-in-Chief: Prof. Haider F. Abdul Amir
    • Executive Editor: Ms. Julia S. Ma
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library, Chemical Abstracts Services (CAS), Ulrich's Periodicals Directory, Google Scholar, ProQuest and DOAJ.
    • E-mail: jocet@ejournal.net
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  • Jun 13, 2017 News! JOCET Vol. 5, No. 4 is available online now. 15 papers which cover 4 specific areas are published in this issue.   [Click]
Editor-in-chief
School of Science and Technology Universiti Malaysia Sabah, Malaysia.
I would like to express my appreciation to all authors, reviewers and edtors.
JOCET 2017 Vol.5(3): 228-235 ISSN: 1793-821X
DOI: 10.18178/JOCET.2017.5.3.374

The Prediction of Wind Power Generation: A Case Study in South Korea

Jun-Sung Kim, Jin-Ho Shin, Young-Bae Park, and Hee-Jeong Park
Abstract—It is difficult to predict the power generation amount of wind turbines because it is depending on weather condition. However it is important to predict wind power generation amount accurately, because it is possible to maximize the utilization of wind turbines, and also establish more effective operation plan of grid. The purpose of this study is to increase the accuracy of prediction of the power generation amount. To increase accuracy, we built the 3 dimensional weather research and forecasting model that is the most applicable to our developing district environment. In case of short-term prediction within 6 hours, to overcome the weakness that cannot reflect the regional characteristics for particular areas enough, we predicted weather information considering statistical characteristics such as weather pattern change information of wind plants. Using the result of weather prediction as a basic data, we computed the power generation amount by plants and wind generators. In the proposed approaches, inaccuracies are analyzed based on the combination of physical model and statistical model. The result of the analysis showed 15.7% (within 24 hours), and 17.5% (within 48 hours) error rate.

Index Terms—Short and midterm prediction of wind power, wind power generation forecasting system, distributed generation.

Jun-Sung Kim, Jin-Ho Shin, Young-Bae Park, and Hee-Jeong Park are with the Korea Electric Power Research Institute, Daejeon, Munji-Road 105, Korea (e-mail: {junskim, jinho, parkyb, parkhj3}@kepco.co.kr).

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Cite:Jun-Sung Kim, Jin-Ho Shin, Young-Bae Park, and Hee-Jeong Park, "The Prediction of Wind Power Generation: A Case Study in South Korea," Journal of Clean Energy Technologies vol. 5, no. 3, pp. 228-235, 2017.

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