Earticle

초록

영어

With the rapid expansion of renewable energy deployment, power systems are increasingly exposed to issues such as higher output variability. Photovoltaic generation, as the most widely installed variable renewable energy source both domestically and internationally, exhibits significant fluctuations due to weather conditions. These characteristics lead to operational challenges including increased curtailment, higher reserve requirements, and even risks of large-scale outages. This study aimed to improve the accuracy of photovoltaic power generation forecasting by developing a data quality control procedure for meteorological data collected at a PV plant. The quality-controlled data were used as inputs to SVM and XGBoost, resulting in improved forecasting accuracy, with MAPE decreasing from 7–10% to 6.32% and 6.08%, respectively. The results demonstrate that meteorological data quality control significantly enhances PV forecasting performance and can contribute to distributed energy resource operation and curtailment mitigation strategies.

목차

Abstract
1. 서론
2. 국내외 사례
3. 연구방법
3.1 데이터 수집
3.2 기상데이터 분석
3.3 발전량과 온도, 일사량과의 상관관계 분석
3.4 기상데이터의 데이터 QC 알고리즘 개발
4. 해석 결과 및 고찰
4.1 데이터 QC 적용 전 발전량 예측 평가
4.2 데이터 품질관리 적용 기상 데이터 예측
4.3 데이터 품질관리 적용 발전량 예측
5. 결론
후기
References

키워드

저자

• 김은지 [ Eun-Ji Kim | Clean Energy Transition Group, Korea Institute of Industrial Technology (KITECH) ]
• 박성식 [ Sung-Seek Park | Carbon Zero Technology Institute ]
• 전용한 [ Yong-Han Jeon | Dept. of fire protection Engineering, Sangji University ]
• 오승진 [ Seung Jin Oh | Researcher of the Korea Institute of Industrial Technology/Dept. of Convergence Manufacturing System Engineering, University of Science and Technology (UST) ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국기계항공기술학회지(구 한국기계기술학회지) [Journal of the Korean Society of Mechanical and Aviation Technology ]

• 간기

  격월간

• pISSN

  1229-604X

• eISSN

  2508-3805

• 수록기간

  1999~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 550 DDC 620