지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.2
- 수록면
- 222 - 232 (11page)
이용수
초록· 키워드
오류제보하기
The power output of solar panels highly depends on environmental situations like weather and air pollution. Due to bad weather or air pollution, it is difficult for solar panels to operate at their full potential. Knowing the power output of solar panels in advance helps set up the solar panels correctly and work their possible potential. This paper presents an approach to predict the power output of solar panels based on weather and air pollution features using machine learning methods. We create machine learning models with three kinds set of features, such as weather, air pollution, and weather and air pollution. Our datasets are collected from the area of Seoul, South Korea, between 2017 and 2019. The experimental results show that the weather and air pollution features can be efficient factors to predict the power output of solar panels.
목차
ABSTRACT
1. INTRODUCTION
2. RELATED STUDY
3. METHODOLOGY
4. EXPERIMENTAL RESULTS
5. CONCLUSION
REFERENCE
참고문헌 (10)
참고문헌 신청
International Energy Agency, Photovoltaic Power Systems Program: Snapshot of Global PV Markets 2020, Report IEA-PVPS T1- 37, 2020.
J. Ko and H. Kim, “Implementing the Smart System for the Safety of the Elderly and the Weak - Focus on Energy Harvest -,” Journal of Korea Multimedia Society, Vol. 23, No. 12, pp. 1507-1518, 2020.
R. Nageem and R. Jayabarathi, “Predicting the Power Output of a Grid-Connected Solar Panel Using Multi-Input Support Vector Regression,” Procedia Computer Science, Vol. 115, pp. 723-730, 2017.
A. Saberian, H. Hizam, M.A.M. Radzi, M.Z.A.A. Kadir and M. Mirzaei, “Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks,” International J ournal of Photoenergy, Vol. 2014, article. 469701, 2014.
S.G. Kim, J.Y. Jung, and M.K. Sim, “A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning,” Sustainability, Vol. 11, article. 1501, 2019.
J. Ko and H. Kim, “Implementing the Smart System for the Safety of the Elderly and the Weak - Focus on Energy Harvest -,” Journal of Korea Multimedia Society, Vol. 23, No. 12, pp. 1507-1518, 2020.
R. Nageem and R. Jayabarathi, “Predicting the Power Output of a Grid-Connected Solar Panel Using Multi-Input Support Vector Regression,” Procedia Computer Science, Vol. 115, pp. 723-730, 2017.
A. Saberian, H. Hizam, M.A.M. Radzi, M.Z.A.A. Kadir and M. Mirzaei, “Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks,” International J ournal of Photoenergy, Vol. 2014, article. 469701, 2014.
S.G. Kim, J.Y. Jung, and M.K. Sim, “A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning,” Sustainability, Vol. 11, article. 1501, 2019.
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