Journal of Advanced Navigation Technology (한국항행학회논문지)

The Korean Navigation Institute (한국항행학회)
권호 표지
DOI QR코드

DOI QR Code

Forecasting of Short Term Photovoltaic Generation by Various Input Model in Supervised Learning

지도학습에서 다양한 입력 모델에 의한 초단기 태양광 발전 예측

  • 장진혁 (가천대학교 에너지 IT학과) ;
  • 신동하 (가천대학교 에너지 IT학과) ;
  • 김창복 (가천대학교 에너지 IT학과)
  • Received : 2018.09.28
  • Accepted : 2018.10.23
  • Published : 2018.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

This study predicts solar radiation, solar radiation, and solar power generation using hourly weather data such as temperature, precipitation, wind direction, wind speed, humidity, cloudiness, sunshine and solar radiation. I/O pattern in supervised learning is the most important factor in prediction, but it must be determined by repeated experiments because humans have to decide. This study proposed four input and output patterns for solar and sunrise prediction. In addition, we predicted solar power generation using the predicted solar and solar radiation data and power generation data of Youngam solar power plant in Jeollanamdo. As a experiment result, the model 4 showed the best prediction results in the sunshine and solar radiation prediction, and the RMSE of sunshine was 1.5 times and the sunshine RMSE was 3 times less than that of model 1. As a experiment result of solar power generation prediction, the best prediction result was obtained for model 4 as well as sunshine and solar radiation, and the RMSE was reduced by 2.7 times less than that of model 1.

본 연구는 기온, 강수량, 풍향, 풍속, 습도, 운량, 일조, 일사 등 시간별 기상 데이터를 이용하여, 일사 및 일조 그리고 태양광 발전예측을 하였다. 지도학습에서 입출력패턴은 예측에서 가장 중요한 요소이지만 인간이 직접 결정해야하기 때문에, 반복적인 실험에 의해 결정해야 한다. 본 연구는 일사 및 일조 예측을 위하여 4가지 모델의 입출력 패턴을 제안하였다. 또한, 예측된 일조 및 일사 데이터와 전라남도 영암 태양광 발전소의 발전량 데이터를 사용하여 태양광 발전량을 예측하였다. 실험결과 일조 및 일사 예측에서 모델 4가 가장 예측결과가 우수했으며, 모델 1에 비해 일조의 RMSE는 1.5배 정도 그리고 일사의 RMSE는 3배 정도 오차가 줄었다. 태양광 발전예측 실험결과 일조 및 일사와 마찬가지로 모델 4가 가장 예측결과가 좋았으며, 모델 1 보다 RMSE가 2.7배 정도 오차가 줄었다.

Keywords

References

  1. B. H. Lee, “A study on simplified robust optimal operation of microgrids considering the uncertainty of renewable generation and loads,” The Transactions of The Korean Institute of Electrical Engineers, Vol. 66, No. 3, pp. 513-521, May. 2017 https://doi.org/10.5370/KIEE.2017.66.3.513
  2. S. B. Rhee, K. H. Kim, and S. G. Lee, "Optimal operation scheme of microgrid system based on renewable energy resources," The Transactions of the Korean Institute of Electrical Engineers, Vol. 60, No. 8, pp. 1467-1472, Aug. 2011 https://doi.org/10.5370/KIEE.2011.60.8.1467
  3. A. J. Conejo, M. a. Plazas, R. Espinola, S. Member, and A. B. Molina, “Day ahead electricity price forecasting using the wavelet transform and ARIMA models,” IEEE Transactions On Power Systems, Vol. 20, No. 2, pp. 1035-1042, 2005. https://doi.org/10.1109/TPWRS.2005.846054
  4. D. J. Lee, J. P. Lee, C. S. Lee, J. Y. Lim, and P. S. Ji, “Development of PV power prediction algorithm using adaptive neuro-fuzzy model,” The Transactions of the Korean Institute of Electrical Engineers, Vol. 64, No. 4, pp. 246-250, Dec. 2015. https://doi.org/10.5370/KIEEP.2015.64.4.246
  5. W. C. Cha, J. H. Park, U. R. Cho, and J. C. Kim, “Design of generation efficiency fuzzy prediction model using solar power element data,” The transactions of The Korean Institute of Electrical Engineers, Vol. 63, No. 10, pp. 1423-1427, Oct. 2014. https://doi.org/10.5370/KIEE.2014.63.10.1423
  6. C. S. Lee, and P. S. Ji, “Development of daily PV power forecasting models using ELM,” The Transactions of the Korean Institute of Electrical Engineers , Vol. 64P, No. 3, pp. 164-168, Sep. 2015
  7. S. M. Lee, and W. J. Lee, "Development of a system for predicting photovoltaic power generation and detecting defects using machine learning," KIPS Transactions on Computer and Communication Systems, Vol. 5, No. 10, pp.353-360, Oct. 2016. https://doi.org/10.3745/KTCCS.2016.5.10.353
  8. A. Yona, T. Senjyu, T. Funabashi, P. Mandal, and C. H. Kim, “Decision technique of solar radiation prediction applying recurrent neural network for short-term ahead power output of photovoltaic system,” Smart Grid and Renewable Energy, Vol. 4, No. 6A, pp. 32-38, Apr.2013 https://doi.org/10.4236/sgre.2013.46A004
  9. Y. Bengio, "Learning deep architectures for AI," Foundations and Trends in Machine Learning., Vol. 2, Vo. 1, pp. 1-127, Jan. 2009. https://doi.org/10.1561/2200000006
  10. L. Deng and D. Yu, “Deep learning: methods and applications,” Foundations and Trends in Signal Processing, Vol. 7, No. 3-4, pp. 197-387, 2014. https://doi.org/10.1561/2000000039
  11. Y. LeCun, Y. Bengio, and G. Hinton. "Deep learning," Nature, Vol 521.7553, pp. 436-444, 2015 https://doi.org/10.1038/nature14539
  12. K. H. Lee, W. J. Kim, “Forecasting of 24_hours ahead photovoltaic power output using support vector regression,” Journal of Korean Institute of Information Technology, Vol. 14, No. 3, pp. 175-183, May 2016.