Skip to main content
Book cover

International Workshop on Data Analytics for Renewable Energy Integration

DARE 2014: Data Analytics for Renewable Energy Integration pp 108–121Cite as

Systematical Evaluation of Solar Energy Supply Forecasts

Part of the Lecture Notes in Computer Science book series (LNAI,volume 8817)

Abstract

The capacity of renewable energy sources constantly increases world-wide and challenges the maintenance of the electric balance between power demand and supply. To allow for a better integration of solar energy supply into the power grids, a lot of research was dedicated to the development of precise forecasting approaches. However, there is still no straightforward and easy-to-use recommendation for a standardized forecasting strategy. In this paper, a classification of solar forecasting solutions proposed in the literature is provided for both weather- and energy forecast models. Subsequently, we describe our idea of a standardized forecasting process and the typical parameters possibly influencing the selection of a specific model. We discuss model combination as an optimization option and evaluate this approach comparing different statistical algorithms against flexible hybrid models in a case study.

Keywords

  • Solar energy
  • Energy forecast model
  • Classification
  • Ensemble

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-13290-7_9
  • Chapter length: 14 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   44.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-13290-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   59.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.

Notes

  1. 1.

    http://www.ecmwf.int

  2. 2.

    http://www.ncep.noaa.gov

  3. 3.

    http://www.ncdc.noaa.gov

  4. 4.

    http://www.mirabel-project.eu/

  5. 5.

    http://www.robotron.eu/

  6. 6.

    http://www.stevengould.org/software/openforecast/

  7. 7.

    http://www.en-apolda.de

  8. 8.

    http://www.50hertz.com

  9. 9.

    http://wetterstationen.meteomedia.de

References

  1. Alamsyah, T.M.I., Sopian, K., Shahrir, A.: Predicting average energy conversion of photovoltaic system in Malaysia using a simplified method. Renew. Energy 29(3), 403–411 (2004)

    CrossRef  Google Scholar 

  2. Alfares, H.K., Nazeeruddin, M.: Electric load forecasting: literature survey and classification of methods. Int. J. Syst. Sci. 33(1), 23–34 (2002)

    CrossRef  MATH  Google Scholar 

  3. Bacher, P., Madsen, H., Nielsen, H.A.: Online short-term solar power forecasting. Sol. Energy 83(10), 1772–1783 (2009)

    CrossRef  Google Scholar 

  4. Mancuso, A.C.B., Werner, L.: Review of combining forecasts approaches. Indep. J. Manag. Prod. 4(1), 248–277 (2013)

    Google Scholar 

  5. Chen, C., Duan, S., Cai, T., Liu, B.: Online 24-h solar power forecasting based on weather type classification using artificial neural network. Sol. Energy 85(11), 2856–2870 (2011)

    CrossRef  Google Scholar 

  6. Dorvlo, A.S.S., Jervase, J.A., Al-Lawati, A.: Solar radiation estimation using artificial neural networks. Appl. Energy 71(4), 307–319 (2002)

    CrossRef  Google Scholar 

  7. Dunea, A.N., Dunea, D.N., Moise, V.I., Olariu, M.F.: Forecasting methods used for performance’s simulation and optimization of photovoltaic grids. In: IEEE Porto Power Tech Proceedings, Porto, Portugal (2001)

    Google Scholar 

  8. Friedman, J.H.: Multivariate adaptive regression splines. Ann. Stat. 19(1), 1–141 (1991)

    CrossRef  MATH  Google Scholar 

  9. Ge, T., Zdonik, S., Tingjian, G., Stanley, B.Z.: A skip-list approach for efficiently processing forecasting queries. In: Proceedings of the VLDB Endowment, Auckland, New Zealand, pp. 984–995 (2008)

    Google Scholar 

  10. Heinemann, D., Lorenz, E., Girodo, M.: Forecasting of solar radiation. In: Wald, L., Suri, M., Dunlop, E.D. (eds.) Solar Energy Resource Management for Elitricity Generation from Local Level to Global Scale, pp. 83–94. Nova Science Publishers, New York (2006)

    Google Scholar 

  11. Iga, A., Ishihara, Y.: Characteristics and embodiment of the practical use method of monthly temperature coefficient of the photovoltaic generation system. IEEJ Trans. Power Energy 126, 767–775 (2006)

    CrossRef  Google Scholar 

  12. Ji, W., Chee, K.C.: Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN. Sol. Energy 85(5), 808–817 (2011)

    CrossRef  Google Scholar 

  13. Kostylev, V., Pavlovski, A.: Solar power forecasting performance - towards industry standards. In: 1st International Workshop on the Integration of Solar Power into Power Systems, Aarhus, Denmark (2011)

    Google Scholar 

  14. Lorenz, E., Hurka, J., Heinemann, D., Beyer, H.G.: Irradiance forecasting for the power prediction of grid-connected photovoltaic systems. Sel. Top. Appl. Earth Observ. Remote Sens. 2(1), 2–10 (2009)

    CrossRef  Google Scholar 

  15. Lorenz, E., Remund, J., Müller, S.C., Traunmüller, W., Steinmaurer, G., Pozo, D., Ruiz-Arias, J.A., Fanego, V.L., Ramirez, L., Romeo, M.G., et al.: Benchmarking of different approaches to forecast solar irradiance. In: 24th European Photovoltaic Solar Energy Conference, Hamburg, Germany, pp. 1–10 (2009)

    Google Scholar 

  16. Martin, L., Zarzalejo, L.F., Polo, J., Navarro, A., Marchante, R., Cony, M.: Prediction of global solar irradiance based on time series analysis: application to solar thermal power plants energy production planning. Sol. Energy 84(10), 1772–1781 (2010)

    CrossRef  Google Scholar 

  17. Martins, I., Ferreira, P.M., Ruano, A.E.: Estimation and prediction of cloudiness from ground-based all-sky hemispherical digital images. In: Proceedings of the 2nd Ambient Computing Colloquium in Engineering and Education, Faro, Portugal (2011)

    Google Scholar 

  18. Meyer, E.L., van Dyk, E.E.: Development of energy model based on total daily irradiation and maximum ambient temperature. Renew. Energy 21(1), 37–47 (2000)

    CrossRef  Google Scholar 

  19. Mu, Q., Wu, Y., Pan, X., Huang, L., Li, X.: Short-term load forecasting using improved similar days method. In: Asia-Pacific Power and Energy Engineering Conference (APPEEC), ChengDu, China, pp. 1–4 (2010)

    Google Scholar 

  20. Nelder, J., Mead, R.: A simplex method for function minimization. Comput. J. 7(4), 308–313 (1965)

    CrossRef  MATH  Google Scholar 

  21. Ogliari, E., Grimaccia, F., Leva, S., Mussetta, M.: Hybrid predictive models for accurate forecasting in PV systems. Energies 6(4), 1918–1929 (2013)

    CrossRef  Google Scholar 

  22. Reikard, G.: Predicting solar radiation at high resolutions: a comparison of time series forecasts. Sol. Energy 83(3), 342–349 (2009)

    CrossRef  Google Scholar 

  23. Tao, C., Shanxu, D., Changsong, C.: Forecasting power output for grid-connected photovoltaic power system without using solar radiation measurement. In: 2nd IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG), HeFei, China, pp. 773–777 (2010)

    Google Scholar 

  24. Ulbricht, R., Fischer, U., Lehner, W., Donker, H.: Optimized renewable energy forecasting in local distribution networks. In: Proceedings of the EDBT/ICDT Joint Conference Workshops, EDBT ’13, Genua, Italy, pp. 262–266 (2013)

    Google Scholar 

  25. Wolff, B., Lorenz, E., Kramer, O.: Statistical learning for short-term photovoltaic power predictions. In: Proceedings of the ECML/PKDD Joint Conference Workshops, Prague, Czech Republik (2013)

    Google Scholar 

  26. Yona, A., Senjyu, T., Saber, A.Y., Funabashi, T., Sekine, H., Kim, C.-H.: Application of neural network to 24-hour-ahead generating power forecasting for PV system. In: IEEE Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburgh, USA, pp. 1–6 (2008)

    Google Scholar 

  27. Zhang, G.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)

    CrossRef  MATH  Google Scholar 

Download references

Acknowledgment

The work presented in this paper was funded by the European Regional Development Fund (EFRE) and the Free State of Saxony under the grant agreement number 100081313 and co-financed by Robotron Datenbank-Software GmbH. We thank Claudio Hartmann and Andreas Essbaumer for supporting our work.

Author information

Authors and Affiliations

  1. Database Technology Group, Technische Universität Dresden, Dresden, Germany

    Martin Hahmann & Wolfgang Lehner

  2. Robotron Datenbank-Software GmbH, Dresden, Germany

    Robert Ulbricht & Hilko Donker

Authors
  1. Robert Ulbricht
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Martin Hahmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hilko Donker
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wolfgang Lehner
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Robert Ulbricht .

Editor information

Editors and Affiliations

  1. Masdar Inst. of Science and Technology, Abu Dhabi, Utd.Arab.Emir.

    Wei Lee Woon

  2. Masdar Inst. of Science and Technology, Abu Dhabi, Utd.Arab.Emir.

    Zeyar Aung

  3. MIT Sloan School of Management, Cambridge, Massachusetts, USA

    Stuart Madnick

Rights and permissions

Reprints and Permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Ulbricht, R., Hahmann, M., Donker, H., Lehner, W. (2014). Systematical Evaluation of Solar Energy Supply Forecasts. In: Woon, W., Aung, Z., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. DARE 2014. Lecture Notes in Computer Science(), vol 8817. Springer, Cham. https://doi.org/10.1007/978-3-319-13290-7_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-13290-7_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13289-1

  • Online ISBN: 978-3-319-13290-7

  • eBook Packages: Computer ScienceComputer Science (R0)