Journal of the Korean Solar Energy Society (한국태양에너지학회 논문집)

The Korean Solar Energy Society (한국태양에너지학회)
권호 표지
DOI QR코드

DOI QR Code

Trend Review of Solar Energy Forecasting Technique

태양에너지 예보기술 동향분석

  • Cheon, Jae ho (Korea Intellectual Property Strategy Agency, Government Cooperation Team) ;
  • Lee, Jung-Tae (New and Renewable Energy Resource & Policy Center, Korea Institute of Energy Research) ;
  • Kim, Hyun-Goo (New and Renewable Energy Resource & Policy Center, Korea Institute of Energy Research) ;
  • Kang, Yong-Heack (New and Renewable Energy Resource & Policy Center, Korea Institute of Energy Research) ;
  • Yun, Chang-Yeol (New and Renewable Energy Resource & Policy Center, Korea Institute of Energy Research) ;
  • Kim, Chang Ki (New and Renewable Energy Resource & Policy Center, Korea Institute of Energy Research) ;
  • Kim, Bo-Young (New and Renewable Energy Resource & Policy Center, Korea Institute of Energy Research) ;
  • Kim, Jin-Young (New and Renewable Energy Resource & Policy Center, Korea Institute of Energy Research) ;
  • Park, Yu Yeon (Dana Patent Law Firm) ;
  • Kim, Tae Hyun (Dana Patent Law Firm) ;
  • Jo, Ha Na (Dana Patent Law Firm)
  • 전재호 (한국특허전략개발원 정부협력팀) ;
  • 이정태 (한국에너지기술연구원 신재생에너지자원.정책센터) ;
  • 김현구 (한국에너지기술연구원 신재생에너지자원.정책센터) ;
  • 강용혁 (한국에너지기술연구원 신재생에너지자원.정책센터) ;
  • 윤창열 (한국에너지기술연구원 신재생에너지자원.정책센터) ;
  • 김창기 (한국에너지기술연구원 신재생에너지자원.정책센터) ;
  • 김보영 (한국에너지기술연구원 신재생에너지자원.정책센터) ;
  • 김진영 (한국에너지기술연구원 신재생에너지자원.정책센터) ;
  • 박유연 (특허법인다나) ;
  • 김태현 (특허법인다나) ;
  • 조하나 (특허법인다나)
  • Received : 2019.06.11
  • Accepted : 2019.08.12
  • Published : 2019.08.30
Download PDF
⟨ Previous Next ⟩

Abstract

The proportion of solar photovoltaic power generation has steadily increased in the power trade market. Solar energy forecast is highly important for the stable trade of volatile solar energy in the existing power trade market, and it is necessary to identify accurately any forecast error according to the forecast lead time. This paper analyzes the latest study trend in solar energy forecast overseas and presents a consistent comparative assessment by adopting a single statistical variable (nRMSE) for forecast errors according to lead time and forecast technology.

Keywords

References

  1. Kim, S. J., Kim, S. W., and Yoon, Y. T., Master Optimization Process Based on Neural Net Works Ensemble for 24-h Solar Irradiance Forecast, Conference of Information and Control Systems, pp. 209-211, 2017.
  2. Kim, M. J., Lee, D. K., and Kim, G. S., Analysis to reduce PV Generation Output Variation using Energy Storage System, Power Electronics Conference, pp. 113-114, 2013.
  3. Moon, S. I., Economic Assessment of Grid-Connected Energy Storage System, Journal of Electrical World Monthly Magazine, pp. 44-48, 2013.
  4. Park, C. K. and Kim, Y. S., Feasibility Study of P2P Power Trading in Korea, Korea Energy Economics Institute, Research Report 15-10, pp. 1-85, 2016
  5. Spot, E. P. E. X., EPEX Spot. EPEX Spot, Paris, France, accessed Dec, 18, 2017.
  6. Cornaro, C., Pierro, M., and Bucci, F., Master Optimization Process Based on Neural Net Works Ensemble for 24-h Solar Irradiance Forecast, Solar Energy, Vol. 111, pp. 297-312, 2015. https://doi.org/10.1016/j.solener.2014.10.036
  7. Amrouche, B. and Le Pivert, X., Artificial Neural Network Based Daily Local Forecasting for Global Solar Radiation, Applied Energy, Vol. 130, pp. 333-341, 2014. https://doi.org/10.1016/j.apenergy.2014.05.055
  8. Olatomiwa, L., Mekhilef, S., Shamshirband, S., Mohammadi, K., Petkovic, D., and Sudheer, C., A Support Vector Machine-firefly Algorithm-based Model for Global Solar Radiation Prediction, Solar Energy, Vol. 115, pp. 632-644, 2015. https://doi.org/10.1016/j.solener.2015.03.015
  9. Box, G. E., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M., Time Series Analysis: Forecasting and Control, John Wiley & Sons, 2015.
  10. Diagne, M., David, M., Lauret, P., Boland, J., and Schmutz, N., Review of Solar Irradiance Forecasting Methods and A Proposition for Small-scale Insular Grids, Renewable and Sustainable Energy Reviews, Vol. 27, pp. 65-76, 2013. https://doi.org/10.1016/j.rser.2013.06.042
  11. Monteiro, C., Santos, T., Fernandez-Jimenez, L. A., Ramirez-Rosado, I. J., and Terreros-Olarte, M. S., Short-term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity, Energies, Vol. 6, pp. 2624-2643, 2013. https://doi.org/10.3390/en6052624
  12. Tuohy, A., Zack, J., Haupt, S. E., Sharp, J., Ahlstrom, M., Dise, S., Grimit, E., Mohrlen, M., Lange, M., Casado, M. G., Black, J., Marquis, M., and Collier, C., Solar Forecasting: Methods, Challenges, and Performance, IEEE Power and Energy Magazine, Vol. 13, pp. 50-59, 2015.
  13. Ren, Y., Suganthan, P., and Srikanth, N., Ensemble Methods for Wind and Solar Power Forecasting—a State-of-the-art Review, Renewable and Sustainable Energy Reviews, Vol. 50, pp. 82-91, 2015. https://doi.org/10.1016/j.rser.2015.04.081
  14. Azimi, R., Ghayekhloo, M., and Ghofrani, M., A Hybrid Method Based on A New Clustering Technique and Multilayer Perceptron Neural Networks for Hourly Solar Radiation Forecasting, Energy Conversion and Management, Vol. 118, pp. 331-344, 2016. https://doi.org/10.1016/j.enconman.2016.04.009
  15. Sharma, V., Yang, D., Walsh, W., and Reindl, T., Short Term Solar Irradiance Forecasting Using a Mixed Wavelet Neural Network, Renewable Energy, Vol. 90, pp. 481-492, 2016. https://doi.org/10.1016/j.renene.2016.01.020
  16. Alonso-Montesinos, J., Batlles, F. J., and Portillo, C., Solar Irradiance Forecasting at One-minute Intervals for Different sky Conditions Using sky Camera Images, Energy Conversion and Management, Vol. 105, pp. 1166-1177, 2015. https://doi.org/10.1016/j.enconman.2015.09.001
  17. Aguiar, L. M., Pereira, B., David, M., Díaz, F., and Lauret, P. Use of Satellite Data to Improve Solar Radiation Forecasting with Bayesian Artificial Neural Networks, Solar Energy, Vol. 122, pp. 1309-1324, 2015. https://doi.org/10.1016/j.solener.2015.10.041
  18. Notton, G., Voyant, C., Fouilloy, A., Duchaud, J. L., and Nivet, M. L. Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications, Applied Sciences, Vol. 9, No. 1, p. 209, 2019. https://doi.org/10.3390/app9010209
  19. Gutierrez-Corea, F. V., Manso-Callejo, M. A., Moreno-Regidor, M. P., and Manrique-Sancho, M. T. Forecasting Short-term Solar Irradiance Based on Artificial Neural Networks and Data From Neighboring Meteorological Stations, Solar Energy, Vol. 134, pp. 119-131, 2016. https://doi.org/10.1016/j.solener.2016.04.020
  20. Dong, Z., Yang, D., Reindl, T., and Walsh, W. M. A Novel Hybrid Approach Based on Self-organizing Maps, Support Vector Regression and Particle Swarm Optimization to Forecast Solar Irradiance, Energy, Vol. 82, pp. 570-577, 2015. https://doi.org/10.1016/j.energy.2015.01.066
  21. de Paiva, G. M., Pimentel, S. P., Leva, S., and Mussetta, M. Intelligent Approach to Improve Genetic Programming Based Intra-Day Solar Forecasting Models, In 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1-8. IEEE, 2018.
  22. Trapero, J. R., Kourentzes, N., and Martin, A. Short-term Solar Irradiation Forecasting Based on Dynamic Harmonic Regression, Energy, Vol. 84, 289-295, 2015. https://doi.org/10.1016/j.energy.2015.02.100
  23. Di Piazza, A., Di Piazza, M. C., and Vitale, G. Solar and Wind Forecasting by NARX Neural Networks, Renewable Energy and Environmental Sustainability, Vol. 1, p. 39, 2016. https://doi.org/10.1051/rees/2016047
  24. Lima, F. J., Martins, F. R., Pereira, E. B., Lorenz, E., and Heinemann, D. Forecast for Surface Solar Irradiance at the Brazilian Nrtheastern Region Using NWP Model and Artificial Neural Networks, Renewable Energy, Vol. 87, pp. 807-818, 2016. https://doi.org/10.1016/j.renene.2015.11.005
  25. Agoua, X. G., Girard, R., and Kariniotakis, G. Short-term Spatio-temporal Forecasting of Photovoltaic Power Production, IEEE Transactions on Sustainable Energy, Vol. 9, No. 2, pp. 538-546, 2017. https://doi.org/10.1109/tste.2017.2747765
  26. De Giorgi, M. G., Congedo, P. M., and Malvoni, M. Photovoltaic Power Forecasting Using Statistical Methods: Impact of Weather Data, IET Science, Measurement & Technology, Vol. 8, No. 3, pp. 90-97, 2014.
  27. Bouzerdoum, M., Mellit, A., and Pavan, A. M. 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, 2013. https://doi.org/10.1016/j.solener.2013.10.002
  28. Gandelli, A., Grimaccia, F., Leva, S., Mussetta, M., and Ogliari, E. Hybrid Model Analysis and Validation for PV Energy Production Forecasting, In 2014 International Joint Conference on Neural Networks (IJCNN), pp. 1957-1962. IEEE, July, 2014.
  29. Antonanzas, J., Urraca, R., Aldama, A., Fernandez-Jimenez, L. A., and Martinez-de-Pison, F. J., Single and Blended Models for Day-Ahead Photovoltaic Power Forecasting, In International Conference on Hybrid Artificial Intelligence Systems, pp. 427-434, Springer, Cham., June, 2017.

Cited by

  1. Linear-Gompertz Model-Based Regression of Photovoltaic Power Generation by Satellite Imagery-Based Solar Irradiance vol.13, pp.4, 2019, https://doi.org/10.3390/en13040781
  2. A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power vol.13, pp.24, 2019, https://doi.org/10.3390/en13246623