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.

Abstract
1. 서 론
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. 결 론
5. References
저자 소개

• 김은지(한국생산기술연구원 제주기술실용화본부) | Eun-Ji Kim (Korea Institute of Industrial Technology Jeju Technology Application Division)
• 오승진(한국생산기술연구원 제주기술실용화본부) | Seung Jin Oh (Korea Institute of Industrial Technology Jeju Technology Application Division)
• 박성식(탄소중립기술원) | Sung-Seek Park (Carbon Zero Technology Institute)
• 전용한(상지대학교 소방공학과) | Yong-Han Jeon (Department of Fire Protection Engineering, Sangi University) Corresponding author