전기의세계

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지

태양광발전 출력전력 예측기법 연구 동향

  • 배성우 (한양대학교 공과대학 전기.생체공학부 전기공학전공)
  • Published : 2018.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

Keywords

References

  1. K. Balamurugan et al., "Impact of distributed generation on power distribution systems," in Energy Procedia, 2011, pp. 93-100.
  2. M. A. Islam et al., "Global renewable energy-based electricity generation and smart grid system for energy security,"The Scientific World Journal,vol. 2014, pp. 1-13, Aug. 2014.
  3. 산업통상자원부, "재생에너지 3020 이행계획(안)," 2017.12.
  4. Renewables 2016 Global status report. Available: http://www.ren21.net/wp-content/uploads/2018/06/17-8652_GSR2018_FullReport_web_final_.pdf.
  5. Zhen-yu Zhao, Shuang-Ying Zhang, Bryan Hubbard, Xue Yao, "The emergence of the solar photovoltaic power industry in China," Renewable and Sustainable Energy Reviews, Volume 21, 2013, Pages 229-236, May. 2013. https://doi.org/10.1016/j.rser.2012.12.066
  6. H. Yang et al., "A weather-based hybrid method for 1-day ahead hourly forecasting of PV power output," IEEE Trans. Sustainable Energy, vol. 5, no. 3, July 2014.
  7. A. Mellit, and A. M. Pavan, "A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy," Solar Energy, vol. 84, pp. 807-821, May 2010. https://doi.org/10.1016/j.solener.2010.02.006
  8. E. G. Kardakos et al., "Application of time series and artificial neural network models in short-term forecasting of PV power generation," in UPEC, Dublin, Ireland, 2013, pp. 1-6.
  9. J. Shi et. al., "Forecasting power output of photovoltaic system based on weather classification and support vector machines," IEEE Trans. Industry Applicat., vol. 48, no. 3, pp. 1064-1069, May/June 2012. https://doi.org/10.1109/TIA.2012.2190816
  10. M. Rana et al., "Solar power forecasting using weather type clustering and ensembles of neural networks," in IJCNN, Vancouver, BC, Canada, 2016, pp. 1-8.
  11. Z. Wang et al., "Clustering based methods for solar power forecasting," in IJCNN, Vancouver, BC, Canada, 2016, pp. 1-8.
  12. E. Izgi et al., "Short-mid-term solar power prediction by using artificial neural networks," Solar Energy, vol. 86, pp. 725-733, Feb. 2012. https://doi.org/10.1016/j.solener.2011.11.013
  13. A. Saberian et al., "Modelling and prediction of photovoltaic power output using artificial neural networks," Int. J. Photoenergy, vol. 2014, pp. 1-11, Apr. 2014.
  14. Y. Li et al., "An ARMAX model for forecasting the power output of a grid connected photovoltaic system," Renewable Energy, vol. 66, pp. 78-89, June 2014. https://doi.org/10.1016/j.renene.2013.11.067
  15. M. bouzerdoum et al., "A hybrid model (SARIMA-SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant," Solar Energy, vol. 98, pp. 226-235, Dec. 2013. https://doi.org/10.1016/j.solener.2013.10.002
  16. R. Huang et al., "Solar generation prediction using the ARMA model in a laboratory-level micro-grid," in Int. Conf. Smart Grid Commun., Tainan, Taiwan, 2012, pp. 528-533.
  17. M. Rana et al., "Univariate and multivariate methods for very short-term solar photovoltaic power forecasting," Energy Conversion and Manage., vol. 121, pp. 380-390, Aug. 2016. https://doi.org/10.1016/j.enconman.2016.05.025
  18. A. Fentis et al., "Short-term solar power forecasting using support vector regression and feed-forward NN," in NEWCAS, Strasbourg, France, 2017, pp. 405-408.
  19. Y. Li, and J. Niu, "Forecast of power generation for grid-connected photovoltaic system based on Markov Chain," in APPEEC, Wuhan, China, 2009, pp. 1-4.
  20. Z. Hua et al., "A method to forecast photo-voltaic power outputs based on Markov Chain," in IPEMEC, Dalian, China, 2015, pp. 471-476.
  21. C. Wan et al., "Photovoltaic and solar power forecasting for smart grid energy management," CSEE J. Power and Energy Syst., vol. 1, no. 4, pp. 38-46, Dec. 2015. https://doi.org/10.17775/CSEEJPES.2015.00046
  22. K. Benmouiza, and A. Cheknane, "Small-scale solar radiation forecasting using ARMA and nonlinear autoregressive neural network models," Theoretical and Appl. Climatology, vol. 124, pp. 945-958, May 2016. https://doi.org/10.1007/s00704-015-1469-z
  23. J. L. Torres et al., "Forecast of hourly average wind speed with ARMA models in Navarre (Spain)," Solar Energy, vol. 79, pp. 65-77, July 2005. https://doi.org/10.1016/j.solener.2004.09.013
  24. Y. Kashyap et al., "Comparative study of parameter estimation methods for solar irradiation forecasting," J. Clean energy Technologies, vol. 4, no. 3, pp. 192-196, May 2016.
  25. G. Reikard, "Predicting solar radiation at high resolutions: A comparison of time series forecasts," Solar Energy, vol. 83, pp. 342-349, Mar. 2009. https://doi.org/10.1016/j.solener.2008.08.007
  26. I. Colak et al., "Multi-period prediction of solar radiation using ARMA and ARIMA models," in ICMLA, Miami, FL, USA, 2015, pp. 1045-1049.
  27. M. Akal, "Forecasting Turkey's tourism revenues by ARMAX model," Tourism Manage, vol. 25, pp. 565-580, Oct. 2004. https://doi.org/10.1016/j.tourman.2003.08.001
  28. G. Zhang et al., "Forecasting with artificial neural networks: The state of the art," Int. J Forecasting, vol. 14, pp. 35-62, Mar. 1998. https://doi.org/10.1016/S0169-2070(97)00044-7
  29. N. I. Sapankevych, and R. Sankar, "Time series prediction Using support vector machines: A Survey," IEEE Computational Intell. Mag., vol. 4, May 2009.
  30. U. Thissen et al., "Using support vector machines for time series prediction," Chemometrics and Intell. Laboratory Systs., vol. 69, pp. 35-49, Nov. 2003. https://doi.org/10.1016/S0169-7439(03)00111-4
  31. S. Kucukali, and K. Baris, "Turkey's short-term gross annual electricity demand forecast by fuzzy logic approach," Energy Policy, vol. 38, no. 5, pp. 2438-2445, May 2010. https://doi.org/10.1016/j.enpol.2009.12.037
  32. A. Chugh et al., "Fuzzy logic approach for short term solar energy forecasting, in INDICON, New Delhi, India, 2015, pp. 1-6.
  33. M. B. Arias et al., "Prediction of electric vehicle charging-power demand in realistic urban traffic networks," Appl. Energy, vol. 195, pp. 738-753, June 2017. https://doi.org/10.1016/j.apenergy.2017.02.021