Advertisement

Comparison and Sensitivity Analysis of Methods for Solar PV Power Prediction

  • Mashud RanaEmail author
  • Ashfaqur Rahman
  • Liwan Liyanage
  • Mohammed Nazim Uddin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11154)

Abstract

The variable nature of solar power output from PhotoVoltaic (PV) systems is the main obstacle for penetration of such power into the electricity grid. Thus, numerous methods have been proposed in the literature to construct forecasting models. In this paper, we present a comprehensive comparison of a set of prominent methods that utilize weather prediction for future. Firstly, we evaluate the prediction accuracy of widely used Neural Network (NN), Support Vector Regression (SVR), k-Nearest Neighbours (kNN), Multiple Linear Regression (MLR), and two persistent methods using four data sets for 2 years. We then analyze the sensitivities of their prediction accuracy to 10–25% possible error in the future weather prediction obtained from the Bureau of Meteorology (BoM). Results demonstrate that ensemble of NNs is the most promising method and achieves substantial improvement in accuracy over other prediction methods.

Keywords

Solar power prediction Sensitivity analysis Neural networks Support Vector Regression Nearest neighbours Regression 

References

  1. 1.
    Department of the Environment and Energy, Australian Government: Australian Energy Update (2017). https://www.energy.gov.au/sites/g/files/net3411/f/energy-update-report-2017.pdf. Accessed 05 May 2018
  2. 2.
    European Photovoltaic Industry Association: Connecting the Sun-Solar Photovoltaics on the Road to Large Scale Grid Integration. http://www.pvtrin.eu/assets/media/PDF/Publications/other_publications/263.pdf. Accessed 01 Aug 2017
  3. 3.
    Rana, M., Koprinska, I., Agelidis, V.G.: Univariate and multivariate methods for very short-term solar photovoltaic power forecasting. Energy Convers. Manag. 121, 380–390 (2016)CrossRefGoogle Scholar
  4. 4.
    Chu, Y., Urquhart, B., Gohari, S.M.I., Pedro, H.T.C., Kleissl, J., Coimbra, C.F.M.: Short-term reforecasting of power output from a 48 MWe solar PV plant. Sol. Energy 112, 68–77 (2015)CrossRefGoogle Scholar
  5. 5.
    Rana, M., Koprinska, I.: Neural network ensemble based approach for 2D-interval prediction of solar photovoltaic power. Energies 9, 829–845 (2016)CrossRefGoogle Scholar
  6. 6.
    Shi, J., Lee, W.-J., Liu, Y., Yang, Y., Wang, P.: Forecasting power output of photovoltaic systems based on weather classification and support vector machines. IEEE Trans. Ind. Appl. 48, 1064–1069 (2012)CrossRefGoogle Scholar
  7. 7.
    Wang, Z., Koprinska, I., Rana, M.: Clustering based methods for solar power forecasting. In: International Joint Conference on Neural Networks (IJCNN) (2016)Google Scholar
  8. 8.
    Yang, C., Thatte, A., Xie, L.: Multitime-scale data-driven spatio-temporal forecast of photovoltaic generation. IEEE Trans. Sustain. Energy 6, 104–112 (2015)CrossRefGoogle Scholar
  9. 9.
    Yang, D., Ye, Z., Lim, L.H.I., Dong, Z.: Very short term irradiance forecasting using the lasso. Sol. Energy 114, 314–326 (2015)CrossRefGoogle Scholar
  10. 10.
    Pedro, H.T., Coimbra, C.F.: Assessment of forecasting techniques for solar power production with no exogenous inputs. Sol. Energy 86, 2017–2028 (2012)CrossRefGoogle Scholar
  11. 11.
    Long, H., Zhang, Z., Su, Y.: Analysis of daily solar power prediction with data-driven approaches. Appl. Energy 126, 29–37 (2014)CrossRefGoogle Scholar
  12. 12.
    UQ Solar Photovoltaic Data. http://solar.uq.edu.au/user/reportPower.php. Accessed 01 Aug 2017
  13. 13.
    Climate Data Online. http://www.bom.gov.au/climate/data/. Accessed 01 Aug 2017
  14. 14.
    Rana, M., Koprinska, I., Agelidis, V.G.: Forecasting solar power generated by grid connected PV systems using ensembles of neural networks. In: International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland (2015)Google Scholar
  15. 15.
    Rana, M., Koprinska, I., Agelidis, V.G.: Solar power forecasting using weather type clustering and ensembles of neural networks. In: International Joint Conference on Neural Networks (IJCNN), Canada (2016)Google Scholar
  16. 16.
    Rana, M., Koprinska, I., Agelidis, V.G.: 2D-interval forecasts for solar power production. Sol. Energy 122, 191–203 (2015)CrossRefGoogle Scholar
  17. 17.
    Lora, A.T., Santos, J.M.R., Exposito, A.G., Ramos, J.L.M., Santos, J.C.R.: Electricity market price forecasting based on weighted nearest neighbors techniques. IEEE Trans. Power Syst. 22, 1294–1301 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Mashud Rana
    • 1
    Email author
  • Ashfaqur Rahman
    • 2
  • Liwan Liyanage
    • 3
  • Mohammed Nazim Uddin
    • 4
  1. 1.Data61, CSIROSydneyAustralia
  2. 2.Data61, CSIROSandy BayAustralia
  3. 3.School of Computing, Engineering and MathematicsWestern Sydney UniversitySydneyAustralia
  4. 4.Department of Computer Science and EngineeringEast Delta UniversityChittagongBangladesh

Personalised recommendations