신재생에너지 예측시장 참여형 태양광 발전량 예측 모델 개발
Renewable Energy Prediction Market Participation Type Photovoltaics Prediction Model Development
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초록
For stable operation of the power system, predicting the amount of renewable energy generated can help establish a reasonable power generation plan. Solar photovoltaic power generation cannot be arbitrarily controlled and is directly affected by climatic conditions. Therefore, this study introduces a solar photovoltaic power generation forecasting technique that can reduce forecasting errors and maximize settlement income. This power generation forecasting technique uses easily accessible public meteorological data. Participation in the renewable energy generation prediction market is expected to be activated through this study, and various business models could possibly be developed by lowering the entry barrier for participation in the solar photovoltaic power generation prediction market.
Abstract
For stable operation of the power system, predicting the amount of renewable energy generated can help establish a reasonable power generation plan. Solar photovoltaic power generation cannot be arbitrarily controlled and is directly affected by climatic conditions. Therefore, this study introduces a solar photovoltaic power generation forecasting technique that can reduce forecasting errors and maximize settlement income. This power generation forecasting technique uses easily accessible public meteorological data. Participation in the renewable energy generation prediction market is expected to be activated through this study, and various business models could possibly be developed by lowering the entry barrier for participation in the solar photovoltaic power generation prediction market.
참고문헌 (7)
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- "Introduction of Renewable Energy Generation Prediction System" (2020)[URL]
- "Short-term Forecast" (2022)[URL]
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- 이재희, "CMIP6 대응 새로운 기후변화 시나리오의 국제 데이터노드 구축", 한국기후변화학회지, 11(4), pp.247~257 (2020), 한국기후변화학회
- Alba Vilanova, "Linear-Gompertz Model-Based Regression of Photovoltaic Power Generation by Satellite Imagery-Based Solar Irradiance", Energies, 13(4), pp.781~ (2020), MDPI AG[DOI]