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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.8
- 수록면
- 944 - 955 (12page)
- DOI
- 10.9717/kmms.2023.26.8.944
이용수
초록· 키워드
오류제보하기
Environmental problems such as global warming due to excessive use of fossil fuels are becoming serious. In order to solve this problem, the supply of new and renewable energy is being activated, and the new and renewable energy market is also expanding. In particular, the share of solar and wind energy among new and renewable energies is rapidly increasing. However, uncertainty and volatility are inherent in renewable energy due to the characteristics of power generation that depend on natural conditions. This leads to a problem in which errors occur in the prediction of the amount of reserve energy required to secure the amount and cost of renewable energy generation. In this paper, we propose an ensemble-based solar power generation prediction system applying missing value interpolation algorithm. It predicts the amount of solar power generation by using weather forecast data from the Korea Meteorological Administration, and provides visualization and scheduling functions for the amount of power generation and predicted amount through a web page.
목차
ABSTRACT
1. 서론
2. 관련 연구
3. 제안하는 태양광 발전량 분석 및 예측 시스템
4. 실험 결과 및 고찰
5. 결론
REFERENCE
참고문헌 (21)
참고문헌 신청
IEA, World Energy Outlook 2022(2022), https://doi.org/10.1787/3a469970-en (accessed July 3, 2023).
Analysis of International New and Renewable EnergyPolicy and Market(2023), https://lib.keei.re.kr:448/search/DetailView.ax?cid=809069 (accessed July 4, 2023).
W.G. Park, J.S. Kim, S.M. Lim, and C.H. Kim, “A Study on Photovoltaic Output Prediction Uncertainty and Intermittency Compensa-tion Method,” The Transactions of the Korean Institute of Electrical Engineers, Vol. 70, No. 7, pp. 961-968, 2021.
G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, et al., “LightGBM: A Highly Efficient Gradient Boosting Decision Tree,” Advances in Neural Information Processing Systems 30, pp. 3146-3154, 2017.
T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794, 2016.
Analysis of International New and Renewable EnergyPolicy and Market(2023), https://lib.keei.re.kr:448/search/DetailView.ax?cid=809069 (accessed July 4, 2023).
W.G. Park, J.S. Kim, S.M. Lim, and C.H. Kim, “A Study on Photovoltaic Output Prediction Uncertainty and Intermittency Compensa-tion Method,” The Transactions of the Korean Institute of Electrical Engineers, Vol. 70, No. 7, pp. 961-968, 2021.
G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, et al., “LightGBM: A Highly Efficient Gradient Boosting Decision Tree,” Advances in Neural Information Processing Systems 30, pp. 3146-3154, 2017.
T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794, 2016.
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UCI(KEPA) : I410-ECN-0102-2023-004-002035532