머신러닝을 이용한 수상태양광 발전량 예측에 관한 연구
A Study on the Prediction of Floating Photovoltaic System using Machine Learning
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초록
The government is implementing a policy to expand eco-friendly energy as a power source. However, the output of new and renewable energy is not constant. It is difficult to stably adjust the power supply to the power demand in the power system. Therefore, the government predicts day-ahead the amount of renewable energy generation to cope with the output volatility caused by the expansion of renewable energy. It is a system that pays a settlement amount if it transitions within a certain error rate the next day. In this paper, Machine Learning was used to study the prediction of power generation within the error rate.
Abstract
The government is implementing a policy to expand eco-friendly energy as a power source. However, the output of new and renewable energy is not constant. It is difficult to stably adjust the power supply to the power demand in the power system. Therefore, the government predicts day-ahead the amount of renewable energy generation to cope with the output volatility caused by the expansion of renewable energy. It is a system that pays a settlement amount if it transitions within a certain error rate the next day. In this paper, Machine Learning was used to study the prediction of power generation within the error rate.
참고문헌 (8)
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- Hyun-Han Kim, "A Study on Operating Characteristics and Design Factors of Floating Photovoltatic Generating Facilities", The Transactions of the Korea Institute of Electrical Engineers, 66(10), pp.1532~1539 (2017)
- Tae-Heun Kwon, "Effect on Power Generation of Floating Photovoltaic Power System Power byWater Level Change", Journal of the Korean Solar Energy Society, 42(2) (2022)
- Hye-Young Shin, "The efficiency of solacell related to temperature, The Korea Institute of Illuminating and Electrical Installation Engineers", Annual Spring Conference (2009)
- "Introduction of Renewable Energy Generation Prediction System, Electricity Market Operation Rules" (2023)[URL]
- Ref. Mayer, M. J., "Extensive comparison of physical models for photovoltaic power forecasting", Applied Energy, 283, pp.116239~ (2021)
- "Interpretable machine learning in A Guide for Making Black Box Models Explainable" (2019)[URL]