Renewable Energy Prediction Market Participation Type Photovoltaics Prediction Model Development

Yu Jae-Hyeok, Kim Sang-jin, Jang Byung-Hoon, Woo Sung-Min

doi:10.7836/kses.2022.42.6.001

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2022 Vol.42, Issue 6 Next Page

Research Article

Renewable Energy Prediction Market Participation Type Photovoltaics Prediction Model Development 신재생에너지 예측시장 참여형 태양광 발전량 예측 모델 개발

30 December 2022. pp. 1-13

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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.

Keywords

Photovoltaic

Korea Power Exchange Incentive

Deep Learning

Long Short-Term Memory

Public Data

Renewable Energy Prediction System

키워드

태양광 발전량 예측

한국전력거래소 인센티브

심층 학습

장단기 기억 메모리

공공 데이터

재생에너지 예측 제도

References

Ministry of Trade, Industry and Energy, Renewable energy 3020 implementation plan is progressing without any setback, and the proportion of solar power generation is steadily increasing, 2020. https://www.motie.go.kr/motie/ne/presse/press2/bbs/bbsView.do?bbs_cd_n=81&bbs_seq_n=163303 last accessed on the 16^th September 2022.

Lee, D. H. and Kim, K. H., Deep Learning Based Prediction Method of Long-term Photovoltaic Power Generation Using Meteorological and Seasonal Information, The Journal of Society for e-Business Studies, Vol. 24, No. 1, pp. 1-16, 2019, https://doi.org/10.7838/jsebs.2019.24.1.001 10.7838/jsebs.2019.24.1.001

Ministry of Trade, Industry and Energy, Introduction of Renewable Energy Generation Prediction System, 2020. https://motie.go.kr/motie/gov3.0/gov_openinfo/sajun/bbs/bbsView.do?bbs_seq_n=163324&bbs_cd_n=81 last accessed on the 16^th September 2022.

Korea Meteorological Agency, Short-term Forecast, 2022. https://data.kma.go.kr/data/rmt/rmtList.do?code=420&pgmNo=572 last accessed on the 16^th September 2022.

University Corporation for Atmospheric Research, THREDDS Data Server (TDS), 2022. https://github.com/Unidata/tds last accessed on the 16^th September 2022.

Lee, J. H., Sung, H. M., Shim, S. B., and Boo, K. O., International Data Node System for CMIP6 Climate Change Projections, Journal of Climate Change Research, Vol. 11, No. 4, pp. 247-257, 2020. http://dx.doi.org/10.15531/KSCCR.2020.11.4.247 10.15531/KSCCR.2020.11.4.247

Vilanova, A., Kim, B. Y., and Kim, C. K., Linear-Gompertz Model-Based Regression of Photovoltaic Power Generation by Satellite Imagery-Based Solar Irradiance, Energies, Vol. 13, No. 4, pp. 781, 2020, https://doi.org/10.3390/en13040781 10.3390/en13040781

Information

Publisher :Korean Solar Energy Society
Publisher(Ko) :한국태양에너지학회
Journal Title :Journal of the Korean Solar Energy Society
Journal Title(Ko) :한국태양에너지학회 논문집
Volume : 42
No :6
Pages :1-13
Received Date :2022. 09. 19
Revised Date :2022. 10. 24
Accepted Date : 2022. 10. 25
DOI :https://doi.org/10.7836/kses.2022.42.6.001

Journal of the Korean Solar Energy Society ISSN:1598-6411(Print) 2508-3562(Online) 한국태양에너지학회 논문집

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