Day-ahead PV Power Forecast by Hybrid ANN Compared to the Five Parameters Model Estimated by Particle Filter Algorithm

1. Yadav, H.K., Pal, Y., Tripathi, M.M.: Photovoltaic power forecasting methods in smart power grid. In: 2015 Annual IEEE India Conference (INDICON), pp. 1–6, December 2015

   Google Scholar 

2. Mellit, A., Pavan, A.M.: 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 Energ. 84(5), 807–821 (2010). http://www.sciencedirect.com/science/article/pii/S0038092X10000782

   Article  Google Scholar 

3. Reikard, G.: Predicting solar radiation at high resolutions: a comparison of time series forecasts. Solar Energ. 83(3), 342–349 (2009). http://www.sciencedirect.com/science/article/pii/S0038092X08002107

   Article  Google Scholar 

4. İzgi, E., Öztopal, A., Yerli, B., Kaymak, M.K., Şahin, A.D.: Short–mid-term solar power prediction by using artificial neural networks. Solar Energ. 86(2), 725–733 (2012). http://www.sciencedirect.com/science/article/pii/S0038092X11004245

   Article  Google Scholar 

5. Shi, J., Lee, W.-J., Liu, Y., Yang, Y., Wang, P.: Forecasting power output of photovoltaic system based on weather classification and support vector machine. In: 2011 IEEE Industry Applications Society Annual Meeting (IAS), pp. 1–6, October 2011

   Google Scholar 

6. Pedro, H.T., Coimbra, C.F.: Assessment of forecasting techniques for solar power production with no exogenous inputs. Solar Energ. 86(7), 2017–2028 (2012). http://www.sciencedirect.com/science/article/pii/S0038092X12001429

   Article  Google Scholar 

7. Monteiro, C., Fernandez-Jimenez, L.A., Ramirez-Rosado, I.J., Muñoz-Jimenez, A., Lara-Santillan, P.M.: Short-term forecasting models for photovoltaic plants: analytical versus soft-computing techniques. Math. Probl. Eng. 2013, 1–9 (2013)

   Article  Google Scholar 

8. Wang, F., Mi, Z., Su, S., Zhao, H.: Short-term solar irradiance forecasting model based on artificial neural network using statistical feature parameters. Energies 5(5), 1355 (2012). http://www.mdpi.com/1996-1073/5/5/1355

   Article  Google Scholar 

9. Yang, H.T., Huang, C.M., Huang, Y.C., Pai, Y.S.: A weather-based hybrid method for 1-day ahead hourly forecasting of PV power output. IEEE Trans. Sustain. Energ. 5(3), 917–926 (2014)

   Article  Google Scholar 

10. Dolara, A., Leva, S., Manzolini, G.: Comparison of different physical models for PV power output prediction. Solar Energ. 119, 83–99 (2015)

    Article  Google Scholar 

11. Dolara, A., Grimaccia, F., Leva, S., Mussetta, M., Ogliari, E.: A physical hybrid artificial neural network for short term forecasting of PV plant power output. Energies 8(2), 1138–1153 (2015)

    Article  Google Scholar 

12. IEC, Procedures for Temperature and Irradiance Corrections to Measured IV Characteristics (2010)

    Google Scholar 

13. Nelson, J.: The Physics of Solar Cells, vol. 1. World Scientific, Singapore (2003)

    Book  Google Scholar 

14. De Soto, W., Klein, S., Beckman, W.: Improvement and validation of a model for photovoltaic array performance. Solar Energ. 80(1), 78–88 (2006)

    Article  Google Scholar 

15. Hottel, H.C.: A simple model for estimating the transmittance of direct solar radiation through clear atmospheres. Solar Energ. 18(2), 129–134 (1976)

    Article  Google Scholar 

16. Doucet, A., De Freitas, N., Gordon, N.: An introduction to sequential monte carlo methods. In: Doucet, A., et al. (eds.) Sequential Monte Carlo Methods in Practice. Statistics for Engineering and Information Science, pp. 3–14. Springer, New York (2001)

    Chapter  Google Scholar 

Download references