Short term cloud nowcasting for a solar power plant based on irradiance historical data

  • Rafael Caballero University Complutense of Madrid
  • Luis F. Zarzalejo Renewable Energy Division. Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), 28040 Madrid, Spain
  • Álvaro Otero University Complutense de Madrid, 28040 Madrid, Spain
  • Luis Piñuel University Complutense de Madrid, 28040 Madrid, Spain
  • Stefan Wilbert Institute of Solar Research, German Aerospace Center (DLR), 04200 Tabernas, Spain.
Keywords: cloud nowcasting, GHI, LSTM, supervised machine learning


This work considers the problem of forecasting the normal solar irradiance with high spatial and temporal resolution (5 minutes). The forecasting is based on a dataset registered during one year from the high resolution radiometric network at a operational solar power plan at Almeria, Spain. In particular, we show a technique for forecasting the irradiance in the next few minutes from the irradiance values obtained on the previous hour.  Our proposal employs a type of recurrent neural network known as LSTM, which can learn complex patterns and that has proven its usability for forecasting temporal series. The results show a reasonable improvement with respect to other prediction methods typically employed in the studies of temporal series.


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D. Renne, Semi-Annual Status. Task 36: Solar. Resource Knowledge Management. Solar Resource Knowledge Management. 2009.

E Lorenz, A Hammer, and D Heinemann. Short term forecasting of solar radiation based on satellite data. In EUROSUN 2004 (ISES Europe Solar Congress), pages 841- 848, 2004.

Bosch, J. L. y Kleissl, J. Cloud motion vectors from a network of ground sensors in a solar power plant. Solar Energy 95(1), 13-20, 2013.

G. Reikard. Predicting solar radiation at high resolutions: A comparison of time series forecasts. Solar Energy, 83(3):342-349, 2009.

Martín, L., Zarzalejo, L. F., Polo, J., Navarro, A., Marchante, R. y Cony, M. Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning. Solar Energy 84(10), 1772-1781, 2010.

C. Paoli, C, Voyant, M. Muselli, and M.L. Nivet. Forecasting of preprocessed daily solar radiation time series using neural networks. Solar Energy, 84(12), 2146-2160, 2010

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. Solar Energy, 84(5), 807-821, 2010.

M. Bou-Rabee, S. A. Sulaiman, M. Saleh, and S. Marafi. Using artificial neural networks to estimate solar radiation in Kuwait. Renewable and Sustainable Energy Reviews, 72, 434-438. 2017.

M. Diagne, M. David, P. Lauret, J. Boland, and N. Schmutz. Review of solar irradiance forecasting methods and a proposition for small-scale insular grids. Renewable and Sustainable Energy Reviews, 27, 65-76. 2013.

I. A. Walter, R. G. Allen, R. Elliott, M.E. Jensen, D. Itenfisu, B Mecham, ... and T Spofford. ASCE's standardized reference evapotranspiration equation. In Watershed Management and Operations Management 2000 (pp. 1-11). 2000.

T. Caliński, and J. Harabasz A dendrite method for cluster analysis. Communications in Statistics-theory and Methods, 3(1), 1-27. 1974

D. Kwiatkowski, P. C B Phillips, P. Schmidt, and Y. Shin. 1992. Testing the Null Hypothesis of Stationarity Against the Alternative of a Unit Root: How Sure Are We That Economic Time Series Have a Unit Root? Journal of Econometrics 54 (1-3): 159–78. 1992.

R. J. Hyndman and G. Athanasopoulos. Forecasting: principles and practice. OTexts. 2018.

S. Makridakis, S.C. Wheelwright, and R.J. Hyndman. Forecasting: methods and applications. John Wiley & Sons, 1998.

S. Haykin. Neural Networks: A Comprehensive Foundation Upper. Saddle River. NJ, USA, pp. 1–842. 1998.

S. Hochreiter and J. Schmidhuber. Long Short-Term Memory. Neural Computation. 9 (8): 1735–1780. 1997

F. A. Gers , J. Schmidhuber and F. Cummins. Learning to Forget: Continual Prediction with LSTM. Neural Computation Volume 12-10. p.2451-2471. 2000

F. Wilcoxon. Individual Comparisons by Ranking Methods. Biometrics 1, 80-83. 1945

How to Cite
Caballero, R., Zarzalejo, L. F., Otero, Álvaro, Piñuel, L., & Wilbert, S. (2018). Short term cloud nowcasting for a solar power plant based on irradiance historical data. Journal of Computer Science and Technology, 18(03), e21.
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