Abstract
Nowcasting global solar irradiance on very short time horizons is the principal topic discussed in this chapter. Various ARIMA models for nowcasting clearness index are inferred and assessed. Radiometric data measured at 15 s lag during June 2010 in Timisoara (Romania) are used for setting up and testing the models. First-order differencing ARIMA models have proven suitable for nowcasting instantaneous values of the clearness index components, beam and global. The model performance is studied as a function of forecasting time horizon and season. The model’s accuracy goes down with increased time horizon. It is shown that the model’s accuracy increases with the stability of the solar radiative regime. The second subject discussed in this chapter is forecasting daily global solar irradiation for the next day, also using ARIMA models.
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References
Box GEP, Jenkins GM (1970) Time series analysis. Forecasting and control. Holden-Day, San Francisco
Box GEP, Tiao GC (1992) Bayesian Inference in Statistical Analysis (Wiley Classics Library) Wiley-Interscience
Brockwell PJ, Davis RA (2002) Introduction to time series and forecasting. Springer, New York
Chen D, Peace KE (2010) Clinical trial data analysis using R. CRC Press, Boca Raton
De Gooijer JG, Hyndman RJ (2006) 25 years of time series forecasting. Int J Forecast 22:443–473
Gallego AJ, Camacho EF (2012) Estimation of effective solar irradiation using an unscented Kalman filter in a parabolic-trough field. Sol Energy (in press, corrected proofs). doi: 10.1016/j.solener.2011.11.012
Gillespie DT (1991) Markov processes: An introduction for physical scientists. Academic Press, London
Gottman JM (1982) Time-series analysis: a comprehensive introduction for social scientists. Cambridge University Press, Cambridge
Iizumi T, Nishimori M, Yokozawa M, Kotera A, Duy Khang N (2012) Statistical downscaling with bayesian inference: estimating global solar radiation from reanalysis and limited observed data. Int J Climatol 32:464–480
Kalman R (1960) A new approach to linear filtering and prediction problems. J Basic Eng 82:35–45
Palit AK, Popovic D (2005) Computational intelligence in time series forecasting: theory and engineering applications. Springer, Berlin
Paoli C, Voyant C, Muselli M, Nivet M-L (2010) Forecasting of preprocessed daily solar radiation time series using neural networks. Sol Energy 84(12):2146–2160
Pankratz A (1983) Forecasting with univariate Box-Jenkins models. Concepts and cases. Wiley, New York
Poggi P, Notton G, Muselli M, Louche A (2000) Stochastic study of hourly total solar radiation in Corsica using a markov model. Int J Climatol 20:1843–1860
PTC (2012)—MathCAD—Engineering calculations software. http://www.ptc.com/products/mathcad/
Sprott JC (2003) Chaos and time-series analysis. Oxford University Press, Oxford
Statgrafics (2012) Statgraphics centurion http://www.statlets.com/statgraphics_centurion.htm
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Paulescu, M., Paulescu, E., Gravila, P., Badescu, V. (2013). Time Series Forecasting. In: Weather Modeling and Forecasting of PV Systems Operation. Green Energy and Technology. Springer, London. https://doi.org/10.1007/978-1-4471-4649-0_6
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DOI: https://doi.org/10.1007/978-1-4471-4649-0_6
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