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Comparative Analysis of Solar Power Generation Prediction AI Model DNN-RNN

태양광 발전량 예측 인공지능 DNN-RNN 모델 비교분석

  • Hong, Jeong-Jo (Division of Information and Communication Convergence Engineering, Mokwon University) ;
  • Oh, Yong-Sun (Division of Information and Communication Convergence Engineering, Mokwon University)
  • 홍정조 (목원대학교 정보통신융합공학부) ;
  • 오용선 (목원대학교 정보통신융합공학부)
  • Received : 2022.04.11
  • Accepted : 2022.05.28
  • Published : 2022.06.30

Abstract

In order to reduce greenhouse gases, the main culprit of global warming, the United Nations signed the Climate Change Convention in 1992. Korea is also pursuing a policy to expand the supply of renewable energy to reduce greenhouse gas emissions. The expansion of renewable energy development using solar power led to the expansion of wind power and solar power generation. The expansion of renewable energy development, which is greatly affected by weather conditions, is creating difficulties in managing the supply and demand of the power system. To solve this problem, the power brokerage market was introduced. Therefore, in order to participate in the power brokerage market, it is necessary to predict the amount of power generation. In this paper, the prediction system was used to analyze the Yonchuk solar power plant. As a result of applying solar insolation from on-site (Model 1) and the Korea Meteorological Administration (Model 2), it was confirmed that accuracy of Model 2 was 3% higher. As a result of comparative analysis of the DNN and RNN models, it was confirmed that the prediction accuracy of the DNN model improved by 1.72%.

지구 온난화의 주범인 온실가스 감축을 위해 UN은 1992년 기후변화협약을 체결하였다. 우리나라도 온실가스 감축을 위해 재생에너지 보급 확대 정책을 펼치고 있다. 태양에너지를 이용한 재생에너지 개발의 확대는 풍력과 태양광 발전의 확대로 이어졌다. 기상 상황에 영향을 많이 받는 재생에너지 개발의 확대는 전력계통의 수요공급관리에 어려움이 발생하고 있다. 이러한 문제를 해결하기 위해 전력중개시장을 도입하게 되었다. 따라서 전력중개시장 참여를 위해서는 발전량 예측이 필요하다. 본 논문에서는 자체 개발한 예측 시스템을 활용하여 연축태양광발전소에 대하여 분석하였다. 현장 일사량(모델 1)과 기상청 일사량(모델 2)을 적용한 결과 모델 2가 3% 정도 높은 것을 확인하였다. 또한, DNN과 RNN 모델을 비교 분석한 결과 DNN 모델이 예측 정확도가 1.72% 정도 향상되는 것을 확인하였다.

Keywords

References

  1. C. K. Lee, "In the era of carbon neutrality beyond greenhouse gas reduction, renewable energy is the answer", K-Sure Insight, 2021. https://www.ksure.or.kr.
  2. D. H. Lee and K. H. Kim, "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
  3. Renewable Energy Policy Division, "5th Renewable Energy Basic Plan (2020-2034)", 2020. http://www.motie.go.kr.
  4. Y. T. Lee, D. H. Kim, W. S. Sin, C. K. Kim, H. G. Kim, S. W. Han, "A Comparison of Machine Learning Models in Photovoltaic Power Generation Forecasting", Journal of the Korean Institute of Industrial Engineers, Vol.47, No.5, pp.444-458, 2021. https://doi.org/10.7232/JKIIE.2021.47.5.444.
  5. S. H. Lee, "Domestic virtual power plant system and status", Korea Development Bank Monthly Report, Vol.767, No.10, pp.25-47, 2019.
  6. Encyclopedia of Daum, "Photoelectric effect", m.search.daum.net
  7. J. J. Song, Y. S. Jeong & S. H. Lee, "Analysis of prediction model for solar power generation", Journal of Digital Convergence, Vol.12, No.3, pp.243-248, 2014. https://dx.doi.org/10.14400/JDC.2014.12.3.243
  8. J. powers and M. M. Ali, "Application of neural networks in aluminum corrosion", Journal of the Korean Data & Information Science Society, Vol.1, pp.157-172.
  9. S. M. Lee, H. S. Jo, H. H. Lee, G. B. Lee, B. R. Oh & O. K. Kwon, "An Annual Photovoltaic Generation Estimation Considering Meteorogical Elements", KIEE Summer Conference 2018, pp.875-876, 2018.
  10. H. Lee, "Analysis of time series models for consumer price index", Journal of the Korean Data & Information Science Society, Vol.23, No.3, pp.535-542, 2012. https://doi.org/10.7465/jkdi.2012.23.3.535
  11. H. S. Jeong, "Power Generation Prediction Model Considering Environmental Characteristics of the Floating Photovoltaic System", pp.37-39, 2021.
  12. Etsys Ltd, "Power generation forecasting and system building service for small-scale power brokerage transactions", pp.9-10, 2021.
  13. Y. M. Seo, B. J. Lee, Y. Y. Choi, "Machine Learning Model-based Photovoltaic Power Generation Forecasting Using Meteorological Data", Journal of the Korean Society of Environmental Technology, Vol.18, No.3, pp.242-251, 2017.
  14. Y. S. Kim, S. H. Lee, H. W. Kim, "Prediction Method of Photovoltaic Power Generation Based on LSTM Using Weather Information", Journal of the Korean Institute of Communications and Information Sciences, Vol.44, No.12, pp.2231-2238, 2019. https://doi.org/10.7840/kics.2019.44.12.2231
  15. J. Y. Seo, "Deep Learning with TensorFlow", pp.265-282, 2021.