Univariate and multivariate methods for very short-term solar photovoltaic power forecasting

Energy Conversion and Management

Volumn 121, Issue , 2016, Pages 380-390

  Rana, Mashud ^a   Koprinska, Irena ^b   Agelidis, Vassilios G ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

^b UNIVERSITY OF SYDNEY   (Australia)

Author keywords

Neural networks; Solar power forecasting; Support vector regression; Time series prediction; Variable selection

Indexed keywords

ARTIFICIAL INTELLIGENCE; FORECASTING; LEARNING ALGORITHMS; LEARNING SYSTEMS; METEOROLOGY; NEURAL NETWORKS; PHOTOVOLTAIC CELLS; SOLAR CONCENTRATORS; SOLAR ENERGY; SOLAR POWER GENERATION;

POWER FORECASTING; SOLAR PHOTOVOLTAIC POWER; SOLAR PHOTOVOLTAIC SYSTEM; SUPPORT VECTOR REGRESSION (SVR); SUPPORT VECTOR REGRESSION ALGORITHMS; TIME SERIES PREDICTION; VARIABLE SELECTION; VERY SHORT TERM FORECASTING;

WEATHER FORECASTING;

EID: 84969981836     PISSN: 01968904     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enconman.2016.05.025     Document Type: Article

References (32)

1. Global Market Outlook For Solar Power/2015-2019. Solar Power Europe; 2015.
2. Hall MA. Correlation-based feature selection for discrete and numeric class machine learning. In: 17th international conference on machine learning (ICML); 2000. p. 359-66.
3. A.J. Smola, and B. Scholkopf A tutorial on support vector regression Statist Comput 14 2004 199 222
4. Müller KR, Smola AJ, Rätsch G, Schölkopf B, Kohlmorgen J, Vapnik V. Predicting time series with support vector machines. In: Springer, (Ed.), International conference on artificial neural networks (ICANN); 1997. p. 999-1004.
5. 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 Sol Energy 84 2010 807 821
6. H.T.C. Pedro, and C.F.M. Coimbra Assessment of forecasting techniques for solar power production with no exogenous inputs Sol Energy 86 2012 2017 2028
7. J. Liu, W. Fang, X. Zhang, and C. Yang An improved photovoltaic power forecasting model with the assistance of aerosol index data IEEE Trans Sustain Energy 6 2015 434 442
8. E. Lorenz, T. Scheidsteger, J. Hurka, D. Heinemann, and C. Kurz Regional PV power prediction for improved grid integration Progr Photovolt: Res Appl 19 2011 757 771
9. S. Pelland, G. Galanis, and G. Kallos Solar and photovoltaic forecasting through post-processing of the global environmental multiscale numerical weather prediction model Progr Photovolt: Res Appl 21 2013 284 296
10. D. Yang, Z. Ye, L.H.I. Lim, and Z. Dong Very short term irradiance forecasting using the lasso Sol Energy 114 2015 314 326
11. J. Alonso-Montesinos, and F.J. Batles Solar radiation forecasting in the short- and medium-term under all sky conditions Energy 83 2015 387 393
12. C. Rigollier, O. Bauer, and L. Wald On the clear sky model of the ESRA - European Solar Radiation Atlas - with respect to the heliosat method Sol Energy 68 2000 33 48
13. C. Rigollier, M. Lefevre, and L. Wald The method Heliosat-2 for deriving shortwave solar radiation from satellite images Sol Energy 77 2004 159169
14. J. Alonso-Montesinos, F.J. Batlles, and C. Portillo Solar irradiance forecasting at one-minute intervals for different sky conditions using sky camera images Energy Convers Manage 105 2015 1166 1177
15. R. Urraca, J. Antonanzas, M. Alia-Martinez, F.J. Martinez-de-Pison, and F. Antonanzas-Torres Smart baseline models for solar irradiation forecasting Energy Convers Manage 108 2016 539 548
16. C. Chen, S. Duan, T. Cai, and B. Liu Online 24-h solar power forecasting based on weather type classification using artificial neural network Sol Energy 85 2011 2856 2870
17. J. Shi, W.-J. Lee, Y. Lin, Y. Yang, and P. Wang Forecasting power output of photovoltaic systems based on weather classification and support vector machines IEEE Trans Ind Appl 48 2012 1064 1069
18. University of Queensland Solar Data; 2014.
19. H.-T. Yang, C.-M. Huang, Y.-C. Huang, and Y.-S. Huang A weather-based hybrid method for 1-day ahead hourly forecasting of PV power output IEEE Trans Sustain Energy 5 2014 917 926
20. I. Koprinska, M. Rana, and V.G. Agelidis Correlation and instance based feature selection for electricity load forecasting Knowl-Based Syst 81 2015 29 40
21. I. Guyon, and A. Elisseeff An introduction to variable and feature selection J Mach Learn Res 3 2003 1157 1182
22. F. Martínez-Álvarez, A. Troncoso, G. Asencio-Cortés, and J.C. Riquelme A survey on data mining techniques applied to energy time series forecasting Energies 8 2015 13162 13193
23. I. Koprinska, M. Rana, A. Troncoso, and F. Martínez-Álvarez Combining pattern sequence similarity with neural networks for forecasting electricity demand time series International joint conference on neural networks (IJCNN 2013) 2013 IEEE Press Dallas, USA
24. H.S. Hippert, C.E. Pedreira, and R.C. Souza Neural networks for short-term load forecasting: a review and evaluation IEEE Trans Power Syst 16 2001 44 55
25. K. Hornik, M. Stinchcombe, and H. White Multilayer feedforward networks are universal approximators Neural Netw 2 1989 359 366
26. P.J.L. Adeodato, A.L. Arnaud, G.C. Vasconcelos, R.C.L.V. Cunha, and D.S.M.P. Monteiro MLP ensembles improve long term prediction accuracy over single networks Int J Forecast 2011 661 671
27. T.A.E. Ferreira, G.C. Vasconcelos, and P.J.L. Adeodato A new intelligent system methodology for time series forecasting with artificial neural networks Neural Process Lett 28 2008 113 129
28. M.T. Hagan, and M.B. Menhaj Training feedforward networks with the Marquardt algorithm IEEE Trans Neural Networks 5 1994 989 993
29. A. Setiawan, I. Koprinska, and V.G. Agelidis Very short-term electricity load demand forecasting using support vector regression Int joint conference on neural networks (IJCNN) 2009 IEEE Atlanta, USA 2888 2894
30. T. Hastie, R. Tibshirani, and J. Friedman The elements of statistical learning 2009 Springer New York
31. Lorenz E, Remund J, Müller SC, Traunmüller W, Steinmaurer G, Pozo D, et al. Benchmarking of different approaches to forecast solar irradiance. In: European photovoltaic solar energy conference; 2009. p. 4199-208.
32. Rana M, Koprinska I, Troncoso A. Forecasting hourly electricity load profile using neural networks. In: International joint conference on neural networks (IJCNN), Beijing, China; 2014.