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    고해상도 일사량 관측 자료를 이용한 UM-LDAPS 예보 모형 성능평가 = Evaluation of UM-LDAPS Prediction Model for Solar Irradiance by using Ground Observation at Fine Temporal Resolution

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    https://www.riss.kr/link?id=A107105943

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    Day ahead forecast is necessary for the electricity market to stabilize the electricity penetration. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for longer than 12 hours forecast horizon. Korea Meteorological Administration operates the UM-LDAPS model to produce the 36 hours forecast of hourly total irradiance 4 times a day. This study interpolates the hourly total irradiance into 15 minute instantaneous irradiance and then compare them with observed solar irradiance at four ground stations at 1 minute resolution. Numerical weather prediction model employed here was produced at 00 UTC or 18 UTC from January to December, 2018. To compare the statistical model for the forecast horizon less than 3 hours, smart persistent model is used as a reference model. Relative root mean square error of 15 minute instantaneous irradiance are averaged over all ground stations as being 18.4% and 19.6% initialized at 18 and 00 UTC, respectively. Numerical weather prediction is better than smart persistent model at 1 hour after simulation began.
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    Day ahead forecast is necessary for the electricity market to stabilize the electricity penetration. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for longer than 12 hours forecast ...

    Day ahead forecast is necessary for the electricity market to stabilize the electricity penetration. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for longer than 12 hours forecast horizon. Korea Meteorological Administration operates the UM-LDAPS model to produce the 36 hours forecast of hourly total irradiance 4 times a day. This study interpolates the hourly total irradiance into 15 minute instantaneous irradiance and then compare them with observed solar irradiance at four ground stations at 1 minute resolution. Numerical weather prediction model employed here was produced at 00 UTC or 18 UTC from January to December, 2018. To compare the statistical model for the forecast horizon less than 3 hours, smart persistent model is used as a reference model. Relative root mean square error of 15 minute instantaneous irradiance are averaged over all ground stations as being 18.4% and 19.6% initialized at 18 and 00 UTC, respectively. Numerical weather prediction is better than smart persistent model at 1 hour after simulation began.

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    참고문헌 (Reference)

    1 김창기, "태양광 발전 예보를 위한 UM-LDAPS 예보 모형 성능평가" 한국태양에너지학회 39 (39): 71-80, 2019

    2 이영미, "제주 실시간 일사량의 기계학습 예측 기법 연구" 한국환경과학회 26 (26): 521-527, 2017

    3 Kleissl, J., "Solar Energy Forecasting and Resource Assessment" Academic Press 416-,

    4 Diagne, M., "Review of Solar Irradiance Forecasting Methods and a Proposition for Small-scale Insular Grids" 27 : 65-76, 2013

    5 Mathiesen, P., "Evaluation of Numerical Weather Prediction for Intra-day Solar Forecasting in the Continental United States" 85 : 967-977, 2011

    6 Korea Meteorological Administration, "Evaluation of Numerical Weather Prediction System (2016)" 198-, 2016

    7 Sengupta, M., "Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications" National Renewable Energy Laboratory 2017

    1 김창기, "태양광 발전 예보를 위한 UM-LDAPS 예보 모형 성능평가" 한국태양에너지학회 39 (39): 71-80, 2019

    2 이영미, "제주 실시간 일사량의 기계학습 예측 기법 연구" 한국환경과학회 26 (26): 521-527, 2017

    3 Kleissl, J., "Solar Energy Forecasting and Resource Assessment" Academic Press 416-,

    4 Diagne, M., "Review of Solar Irradiance Forecasting Methods and a Proposition for Small-scale Insular Grids" 27 : 65-76, 2013

    5 Mathiesen, P., "Evaluation of Numerical Weather Prediction for Intra-day Solar Forecasting in the Continental United States" 85 : 967-977, 2011

    6 Korea Meteorological Administration, "Evaluation of Numerical Weather Prediction System (2016)" 198-, 2016

    7 Sengupta, M., "Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications" National Renewable Energy Laboratory 2017

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    학술지 이력

    학술지 이력
    연월일 이력구분 이력상세 등재구분
    2023 평가 재인증평가 신청대상 (재인증)
    2020-01-01 등재 등재학술지 선정 (재인증) KCI등재
    2019-01-01 등재 등재후보학술지 유지 (계속평가) KCI등재후보
    2018-01-01 등재 등재후보학술지 유지 (계속평가) KCI등재후보
    2017-12-01 등재 등재후보로 하락 (계속평가) KCI등재후보
    2013-01-01 등재 등재학술지 유지 (등재유지) KCI등재
    2010-01-01 등재 등재학술지 유지 (등재유지) KCI등재
    2008-01-01 등재 등재학술지 유지 (등재유지) KCI등재
    2005-01-01 등재 등재학술지 선정 (등재후보2차) KCI등재
    2004-01-01 등재 등재후보 1차 PASS (등재후보1차) KCI등재후보
    2002-01-01 등재 등재후보학술지 선정 (신규평가) KCI등재후보
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    학술지 인용정보

    학술지 인용정보
    기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
    2016 0.28 0.28 0.28
    KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
    0.27 0.25 0.618 0.26
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