Solar irradiance forecasting using spatio-temporal empirical kriging and vector autoregressive models with parameter shrinkage

Solar Energy

Volumn 103, Issue , 2014, Pages 550-562

  Yang, Dazhi ^a   Dong, Zibo ^a   Reindl, Thomas ^a   Jirutitijaroen, Panida ^a   Walsh, Wilfred M ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

L method; Parameter shrinkage; Space time forecast; Threshold distance

Indexed keywords

DISPERSIONS; INTERPOLATION; RANDOM PROCESSES; RENEWABLE ENERGY RESOURCES; SHRINKAGE; SOLAR RADIATION;

CORRELATION COEFFICIENT; FORECASTING TECHNIQUES; L-METHOD; RENEWABLE ENERGY SOURCE; SPATIO-TEMPORAL DYNAMICS; SPATIO-TEMPORAL FORECASTING; THRESHOLD DISTANCES; VECTOR AUTOREGRESSIVE MODEL;

FORECASTING;

ACCURACY ASSESSMENT; CORRELATION; ELECTRICITY GENERATION; EMPIRICAL ANALYSIS; IRRADIANCE; KRIGING; SOLAR POWER; SPATIOTEMPORAL ANALYSIS; VECTOR;

EID: 84899115815     PISSN: 0038092X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.solener.2014.01.024     Document Type: Article

References (41)

1. Bosch J., Kleissl J. Cloud motion vectors from a network of ground sensors in a solar power plant. Sol. Energy 2013, 95:13-20. http://dx.doi.org/10.1016/j.solener.2013.05.027. .
2. Bosch J., Zheng Y., Kleissl J. Deriving cloud velocity from an array of solar radiation measurements. Sol. Energy 2013, 87:196-203. http://dx.doi.org/10.1016/j.solener.2012.10.020. .
3. Chow C.W., Urquhart B., Lave M., Dominguez A., Kleissl J., Shields J., Washom B. Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed. Sol. Energy 2011, 85:2881-2893.
4. Chu Y., Pedro H.T., Coimbra C.F. Hybrid intra-hour DNI forecasts with sky image processing enhanced by stochastic learning. Sol. Energy Part C 2013, 98:592-603. http://dx.doi.org/10.1016/j.solener.2013.10.020. .
5. Cox T.F., Cox M.A.A. Multidimensional Scaling 1994, Chapman & Hall, Boundary Row-London.
6. Cressie N., Wikle C. Statistics for Spatio-Temporal Data. Wiley Series in Probability and Statistics 2011, Wiley, .
7. de Luna X., Genton M.G. Predictive spatial-temporal models for spatially sparse environmental data. Stat. Sinica 2005, 15:547-568.
8. Dong Z., Yang D., Reindl T., Walsh W.M. Short-term solar irradiance forecasting using exponential smoothing state space model. Energy 2013, 55:1104-1113. http://dx.doi.org/10.1016/j.energy.2013.04.027. .
9. Dong Z., Yang D., Reindl T., Walsh W.M. Satellite image analysis and a hybrid ESSS/ANN model to forecast solar irradiance in the tropics. Energy Convers Manage 2014, 79:66-73. http://dx.doi.org/10.1016/j.enconman.2013.11.043. .
10. Efron B., Hastie T., Johnstone I., Tibshirani R. Least angle regression. Ann. Stat. 2004, 32:407-499. 10.1214/009053604000000067.
11. Gneiting T. Nonseparable, stationary covariance functions for space-time data. J. Am. Stat. Assoc. 2002, 97:590-600. .
12. Horvatic D., Stanley H.E., Podobnik B. Detrended cross-correlation analysis for non-stationary time series with periodic trends. Eur. Lett.(EPL) 2011, 94. 10.1209/0295-5075/94/18007.
13. Inoue, T., Sasaki, T., Washio, T., 2012. Spatio-temporal kriging of solar radiation incorporating direction and speed of cloud movement. In: The 26th Annual Conference of the Japanese Society for Artificial Intelligence, Yamaguchi City. http://kaigi.org/jsai/webprogram/2012/pdf/708.pdf.
14. Kruskal J. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 1964, 29:1-27. 10.1007/BF02289565.
15. Kruskal J. Nonmetric multidimensional scaling: a numerical method. Psychometrika 1964, 29:115-129. 10.1007/BF02289694.
16. Lonij V.P., Brooks A.E., Cronin A.D., Leuthold M., Koch K. Intra-hour forecasts of solar power production using measurements from a network of irradiance sensors. Sol. Energy 2013, 97:58-66. http://dx.doi.org/10.1016/j.solener.2013.08.002. .
17. Ma C. Families of spatio-temporal stationary covariance models. J. Stat. Plann. Infer. 2003, 116:489-501. http://dx.doi.org/10.1016/S0378-3758(02)00353-1. .
18. Ma C. Linear combinations of space-time covariance functions and variograms. IEEE Trans. Signal Process. 2005, 53:857-864. 10.1109/TSP.2004.842186.
19. Marquez R., Coimbra C.F. Intra-hour DNI forecasting based on cloud tracking image analysis. Sol. Energy 2013, 91:327-336. http://dx.doi.org/10.1016/j.solener.2012.09.018. .
20. Mefti A., Adane A., Bouroubi M. Satellite approach based on cloud cover classification: estimation of hourly global solar radiation from meteosat images. Energy Convers Manage 2008, 49:652-659. http://dx.doi.org/10.1016/j.enconman.2007.07.041. .
21. Miller A. Subset Selection in Regression. Monographs on Statistics and Applied Probability 2002, Chapman & Hall/CRC.
22. Murata A., Yamaguchi H., Otani K. A method of estimating the output fluctuation of many photovoltaic power generation systems dispersed in a wide area. Electr. Eng. Jpn. 2009, 166:9-19. 10.1002/eej.20723.
23. Pedro H.T., Coimbra C.F. Assessment of forecasting techniques for solar power production with no exogenous inputs. Sol. Energy 2012, 86:2017-2028. http://dx.doi.org/10.1016/j.solener.2012.04.004. .
24. Perez R., Ineichen P., Moore K., Kmiecik M., Chain C., George R., Vignola F. A new operational model for satellite-derived irradiances: description and validation. Sol. Energy 2002, 73:307-317. http://dx.doi.org/10.1016/S0038-092X(02)00122-6. .
25. Perez R., Ineichen P., Kmiecik M., Moore K., Renne D., George R. Producing satellite-derived irradiances in complex arid terrain. Sol. Energy 2004, 77:367-371. (the American Solar Energy Society's Solar 2003 Special Issue). http://dx.doi.org/10.1016/j.solener.2003.12.016. .
26. Perez R., Kivalov S., Schlemmer J.K.H., Hoff T.E. Short-term irradiance variability: preliminary estimation of station pair correlation as a function of distance. Sol. Energy 2012, 86:2170-2176. http://www.sciencedirect.com/science/article/pii/S0038092X12000928. 10.1016/j.solener.2012.02.027.
27. Podobnik B., Jiang Z.Q., Zhou W.X., Stanley H.E. Statistical tests for power-law cross-correlated processes. Phys. Rev. E 2011, 84:066118. http://dx.doi.org/10.1103/PhysRevE.84.066118. .
28. Porcu E., Mateu J., Saura F. New classes of covariance and spectral density functions for spatio-temporal modelling. Stoch. Environ. Res. Risk Assessm. 2008, 22:65-79. http://dx.doi.org/10.1007/s00477-007-0160-z.
29. R Development Core Team, 2011. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria. <. ISBN: 3-900051-07-0. http://www.R-project.org/.
30. Reikard G. Predicting solar radiation at high resolutions: a comparison of time series forecasts. Sol. Energy 2009, 83:342-349.
31. Salvador S., Chan P. Learning states and rules for detecting anomalies in time series. Appl. Intell. 2005, 23:241-255. http://dx.doi.org/10.1007/s10489-005-4610-3.
32. Sampson P.D., Guttorp P. Nonparametric estimation of nonstationary spatial covariance structure. J. Am. Stat. Assoc. 1992, 87:108-119. .
33. Tibshirani R. Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc. Ser. B (Methodological) 1996, 58:267-288. .
34. Tibshirani R. Regression shrinkage and selection via the lasso: a retrospective. J. Roy. Stat. Soc.: Ser. B (Statistical Methodology) 2011, 73:273-282. http://dx.doi.org/10.1111/j.1467-9868.2011.00771.x.
35. Wasserman L. All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics) 2003, Springer, http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/0387402721.
36. Xu Q., Zhang S.J., Yan D.X. A new circular distribution and its application to wind data. J. Math. Res. 2010, 2:12-17.
37. Yang D., Jirutitijaroen P., Walsh W.M. The estimation of clear sky global horizontal irradiance at the equator. Energy Procedia 2012, 25:141-148. http://dx.doi.org/10.1016/j.egypro.2012.07.019. .
38. Yang D., Jirutitijaroen P., Walsh W.M. Hourly solar irradiance time series forecasting using cloud cover index. Sol. Energy 2012, 86:3531-3543. http://dx.doi.org/10.1016/j.solener.2012.07.029. .
39. Yang D., Dong Z., Nobre A., Khoo Y.S., Jirutitijaroen P., Walsh W.M. Evaluation of transposition and decomposition models for converting global solar irradiance from tilted surface to horizontal in tropical regions. Sol. Energy 2013, 97:369-387. http://dx.doi.org/10.1016/j.solener.2013.08.033. .
40. Yang D., Gu C., Dong Z., Jirutitijaroen P., Chen N., Walsh W.M. Solar irradiance forecasting using spatial-temporal covariance structures and time-forward kriging. Renew. Energy 2013, 60:235-245. http://dx.doi.org/10.1016/j.renene.2013.05.030. .
41. Zou H., Hastie T. Regularization and variable selection via the elastic net. J. Roy. Stat. Soc.: Ser. B (Statistical Methodology) 2005, 67:301-320. http://dx.doi.org/10.1111/j.1467-9868.2005.00503.x.