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.

Abstract
1. 서 론
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. 결 론
후 기
References

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