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Arbitrated Ensemble for Solar Radiation Forecasting

  • Vítor CerqueiraEmail author
  • Luís Torgo
  • Carlos Soares
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10305)

Abstract

Utility companies rely on solar radiation forecasting models to control the supply and demand of energy as well as the operability of the grid. They use these predictive models to schedule power plan operations, negotiate prices in the electricity market and improve the performance of solar technologies in general. This paper proposes a novel method for global horizontal irradiance forecasting. The method is based on an ensemble approach, in which individual competing models are arbitrated by a metalearning layer. The goal of arbitrating individual forecasters is to dynamically combine them according to their aptitude in the input data. We validate our proposed model for solar radiation forecasting using data collected by a real-world provider. The results from empirical experiments show that the proposed method is competitive with other methods, including current state-of-the-art methods used for time series forecasting tasks.

Keywords

Solar radiation forecasting Renewable energy Ensemble methods Metalearning Time series 

Notes

Acknowledgements

This work is financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013; Project “NORTE-01-0145-FEDER-000036” is financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF).

References

  1. 1.
    Bacher, P., Madsen, H., Nielsen, H.A.: Online short-term solar power forecasting. Sol. Energy 83(10), 1772–1783 (2009)CrossRefGoogle Scholar
  2. 2.
    Bell, R.M., Koren, Y., Volinsky, C.: The bellKor 2008 solution to the netflix prize. Statistics Research Department at AT&T Research (2008)Google Scholar
  3. 3.
    Boland, J.: Time series modelling of solar radiation. In: Badescu, V. (ed.) Modeling Solar Radiation at the Earths Surface, pp. 283–312. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Brazdil, P., Carrier, C.G., Soares, C., Vilalta, R.: Metalearning: Applications to Data Mining. Springer Science & Business Media, Heidelberg (2008)zbMATHGoogle Scholar
  5. 5.
    Brown, G.: Ensemble learning. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 312–320. Springer, Boston (2010)Google Scholar
  6. 6.
    Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(1), 1–22 (2010)CrossRefGoogle Scholar
  7. 7.
    Gama, J., Kosina, P.: Tracking recurring concepts with meta-learners. In: Lopes, L.S., Lau, N., Mariano, P., Rocha, L.M. (eds.) EPIA 2009. LNCS, vol. 5816, pp. 423–434. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-04686-5_35 CrossRefGoogle Scholar
  8. 8.
    Huang, J., Korolkiewicz, M., Agrawal, M., Boland, J.: Forecasting solar radiation on an hourly time scale using a coupled autoregressive and dynamical system (cards) model. Sol. Energy 87, 136–149 (2013)CrossRefGoogle Scholar
  9. 9.
    Hyndman, R.J., Athanasopoulos, G., Razbash, S., Schmidt, D., Zhou, Z., Khan, Y., Bergmeir, C., Wang, E.: Forecast: Forecasting functions for time series and linear models, R package version 5.6 (2014)Google Scholar
  10. 10.
    Karatzoglou, A., Smola, A., Hornik, K., Zeileis, A.: kernlab - an S4 package for kernel methods in R. J. Stat. Softw. 11(9), 1–20 (2004)CrossRefGoogle Scholar
  11. 11.
    Kemmoku, Y., Orita, S., Nakagawa, S., Sakakibara, T.: Daily insolation forecasting using a multi-stage neural network. Sol. Energy 66(3), 193–199 (1999)CrossRefGoogle Scholar
  12. 12.
    Kennel, M.B., Brown, R., Abarbanel, H.D.: Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys. Rev. A 45(6), 3403 (1992)CrossRefGoogle Scholar
  13. 13.
    Koppel, M., Engelson, S.P.: Integrating multiple classifiers by finding their areas of expertise. In: AAAI-1996 Workshop On Integrating Multiple Learning Models, pp. 53–58. Citeseer (1996)Google Scholar
  14. 14.
    Kuhn, M., Weston, S., Keefer, C., Quinlan, R.: Cubist: Rule- and Instance-Based Regression Modeling, R package version 0.0.18 (2014)Google Scholar
  15. 15.
    Lemke, C., Gabrys, B.: Meta-learning for time series forecasting and forecast combination. Neurocomputing 73(10), 2006–2016 (2010)CrossRefGoogle Scholar
  16. 16.
    Lorenz, E., Hurka, J., Heinemann, D., Beyer, H.G.: Irradiance forecasting for the power prediction of grid-connected photovoltaic systems. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2(1), 2–10 (2009)CrossRefGoogle Scholar
  17. 17.
    Maxey, C., Andreas, A.: Oak ridge national laboratory (ORNL); rotating shadowband radiometer (RSR); oak ridge, tennessee (DATA). Technical report, National Renewable Energy Laboratory (NREL), Golden, CO, USA (2007)Google Scholar
  18. 18.
    Mellit, A., Pavan, A.M.: 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(5), 807–821 (2010)CrossRefGoogle Scholar
  19. 19.
    Mendes-Moreira, J., Soares, C., Jorge, A.M., Sousa, J.F.D.: Ensemble approaches for regression: a survey. ACM Comput. Surv. (CSUR) 45(1), 10 (2012)CrossRefzbMATHGoogle Scholar
  20. 20.
    Milborrow, S.: Earth: Multivariate Adaptive Regression Spline Models. Derived from mda:mars by Trevor Hastie and Rob Tibshirani (2012)Google Scholar
  21. 21.
    Oliveira, M., Torgo, L.: Ensembles for time series forecasting. In: ACML Proceedings of Asian Conference on Machine Learning. JMLR: Workshop and Conference Proceedings (2014)Google Scholar
  22. 22.
    Ortega, J., Koppel, M., Argamon, S.: Arbitrating among competing classifiers using learned referees. Knowl. Inf. Syst. 3(4), 470–490 (2001)CrossRefzbMATHGoogle Scholar
  23. 23.
    Reikard, G.: Predicting solar radiation at high resolutions: a comparison of time series forecasts. Sol. Energy 83(3), 342–349 (2009)CrossRefGoogle Scholar
  24. 24.
    Ridgeway, G.: GBM: Generalized Boosted Regression Models, R package version 2.1.1 (2015)Google Scholar
  25. 25.
    Rossi, A.L.D., de Leon Ferreira, A.C.P., Soares, C., De Souza, B.F., et al.: Metastream: a meta-learning based method for periodic algorithm selection in time-changing data. Neurocomputing 127, 52–64 (2014)CrossRefGoogle Scholar
  26. 26.
    dos Santos, P.M., Ludermir, T.B., Prudencio, R.B.C.: Selection of time series forecasting models based on performance information. In: Fourth International Conference on Hybrid Intelligent Systems, HIS 2004, pp. 366–371. IEEE (2004)Google Scholar
  27. 27.
    Sfetsos, A., Coonick, A.: Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques. Sol. Energy 68(2), 169–178 (2000)CrossRefGoogle Scholar
  28. 28.
    Soares, C., Brazdil, P.B.: Zoomed ranking: selection of classification algorithms based on relevant performance information. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds.) PKDD 2000. LNCS, vol. 1910, pp. 126–135. Springer, Heidelberg (2000). doi: 10.1007/3-540-45372-5_13 CrossRefGoogle Scholar
  29. 29.
    Takens, F.: Detecting strange attractors in turbulence. In: Rand, D., Young, L.-S. (eds.) Dynamical Systems and Turbulence, Warwick 1980. LNM, vol. 898, pp. 366–381. Springer, Heidelberg (1981). doi: 10.1007/BFb0091924 CrossRefGoogle Scholar
  30. 30.
    Torgo, L.: An Infra-Structure for Performance Estimation and Experimental Comparison of Predictive Models, R package version 0.1.1 (2013)Google Scholar
  31. 31.
    Venables, W.N., Ripley, B.D.: Modern Applied Statistics with S, 4th edn. Springer, New York (2002). ISBN 0-387-95457-0CrossRefzbMATHGoogle Scholar
  32. 32.
    Wang, X., Smith-Miles, K., Hyndman, R.: Rule induction for forecasting method selection: meta-learning the characteristics of univariate time series. Neurocomputing 72(10), 2581–2594 (2009)CrossRefGoogle Scholar
  33. 33.
    Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241–259 (1992)CrossRefGoogle Scholar
  34. 34.
    Wright, M.N.: Ranger: A Fast Implementation of Random Forests, R package version 0.3.0 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Vítor Cerqueira
    • 1
    • 2
    Email author
  • Luís Torgo
    • 1
    • 2
  • Carlos Soares
    • 1
    • 2
  1. 1.INESC TECPortoPortugal
  2. 2.University of PortoPortoPortugal

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