Earticle

초록

영어

As renewable energy penetration continues to increase, the output variability and forecasting uncertainty of photovoltaic generation have emerged as major operational risks in power systems. This study establishes a sensor-based data quality control procedure to ensure the reliability of meteorological data collected at a PV plant. For temperature, humidity, and wind speed, a four stage QC process physical range check, persistence check, step change check, and median filtering was applied. Solar radiation, which exhibits strong temporal and distributional characteristics, was processed using a three-stage QC procedure consisting of physical range, step change, and frequency distribution checks. Using the quality-controlled meteorological data, PV generation forecasting was performed with SVM and XGBoost models. As a result, the MAPE values improved to 6.32% for SVM and 6.08% for XGBoost after QC application. The findings confirm that meteorological data quality control significantly enhances PV forecasting accuracy and can support future strategies for distributed energy resource management, curtailment mitigation, and power system risk reduction.

목차

Abstract
1. 서론
2. 연구방법
2.1 데이터 수집
2.2 기상데이터 분석
2.3 기상데이터의 데이터 QC 알고리즘 개발
3. 해석 결과 및 고찰
3.1 데이터 QC 적용 전 발전량 예측 평가
3.2 데이터 QC 적용 기상 예측 평가
3.3 데이터 QC 적용 태양광 발전량 예측
4. 결론
5. References

키워드

저자

• 김은지 [ Eun-Ji Kim | 한국생산기술연구원 제주기술실용화본부 ]
• 박성식 [ Sung-Seek Park | 탄소중립기술원 ]
• 오승진 [ Seung Jin Oh | 한국생산기술연구원 제주기술실용화본부 ]
• 전용한 [ Yong-Han Jeon | 상지대학교 소방공학과 ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  대한안전경영과학회지 [Journal of Korea Safety Management & Science]

• 간기

  계간

• pISSN

  1229-6783

• eISSN

  2288-1484

• 수록기간

  1999~2025

• 십진분류

  KDC 530 DDC 620