Advertisement

Gradient Boosting Models for Photovoltaic Power Estimation Under Partial Shading Conditions

  • Nikolaos Nikolaou
  • Efstratios Batzelis
  • Gavin Brown
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10691)

Abstract

The energy yield estimation of a photovoltaic (PV) system operating under partially shaded conditions is a challenging task and a very active area of research. In this paper, we attack this problem with the aid of machine learning techniques. Using data simulated by the equivalent circuit of a PV string operating under partial shading, we train and evaluate three different gradient boosted regression tree models to predict the global maximum power point (MPP). Our results show that all three approaches improve upon the state-of-the-art closed-form estimates, in terms of both average and worst-case performance. Moreover, we show that even a small number of training examples is sufficient to achieve improved global MPP estimation. The methods proposed are fast to train and deploy and allow for further improvements in performance should more computational resources be available.

Keywords

Gradient boosting Solar energy Photovoltaic (PV) system Maximum power point (MPP) Partial shading Machine learning 

Notes

Acknowledgements

This project was partially supported by the EPSRC Centre for Doctoral Training [EP/I028099/1] & the EPSRC LAMBDA [EP/N035127/1] & Anyscale Apps [EP/L000725/1] project grants. N. Nikolaou acknowledges the support of the EPSRC Doctoral Prize Fellowship. E. Batzelis carried out this research at NTUA, Athens, Greece under the support of the ‘IKY Fellowships of Excellence for Postgraduate Studies in Greece-Siemens Program’.

References

  1. 1.
    Batzelis, E., Kampitsis, G.E., Papathanassiou, S.A.: A MPPT algorithm for partial shading conditions employing curve fitting. In: EU PVSEC, pp. 1502–1507 (2016)Google Scholar
  2. 2.
    Batzelis, E.I., Georgilakis, P.S., Papathanassiou, S.A.: Energy models for photovoltaic systems under partial shading conditions: a comprehensive review. IET Renew. Power Gener. 9(4), 340–349 (2015)CrossRefGoogle Scholar
  3. 3.
    Batzelis, E.I., Routsolias, I.A., Papathanassiou, S.A.: An explicit PV string model based on the Lambert W function and simplified MPP expressions for operation under partial shading. IEEE Trans. Sustain. Energy 5(1), 301–312 (2014)CrossRefGoogle Scholar
  4. 4.
    Brecl, K., Topič, K., Topič, M.: Self-shading losses of fixed free-standing PV arrays. Renew. Energy 36(11), 3211–3216 (2011)CrossRefGoogle Scholar
  5. 5.
    Busa-Fekete, R., Kégl, B., Éltető, T., Szarvas, G.: Ranking by calibrated AdaBoost. In: Proceedings of the Learning to Rank Challenge, pp. 37–48 (2011)Google Scholar
  6. 6.
    Caruana, R., Niculescu-Mizil, A.: An empirical comparison of supervised learning algorithms. In: ICML, pp. 161–168 (2006)Google Scholar
  7. 7.
    Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: SIGKDD, pp. 785–794 (2016)Google Scholar
  8. 8.
    Deline, C., Dobos, A., Janzou, S., Meydbray, J., Donovan, M.: A simplified model of uniform shading in large photovoltaic arrays. Sol. Energy 96, 274–282 (2013)CrossRefGoogle Scholar
  9. 9.
    Dolan, J.A., Lee, E., Yeh, C., Ben-Menahem, S., Ishihara, A.K.: Neural network estimation of photovoltaic IV curves under partially shaded conditions. In: IJCNN, pp. 1358–1365 (2011)Google Scholar
  10. 10.
    Fernández-Delgado, M., Cernadas, E., Barro, S., Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems? JMLR 15, 3133–3181 (2014)MATHMathSciNetGoogle Scholar
  11. 11.
    Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2000)CrossRefMATHMathSciNetGoogle Scholar
  12. 12.
    Mason, L., Baxter, J., Bartlett, P., Frean, M.: Boosting algorithms as gradient descent. In: NIPS, pp. 512–518. MIT Press (2000)Google Scholar
  13. 13.
    Moballegh, S., Jiang, J.: Modeling, prediction, and experimental validations of power peaks of PV arrays under partial shading conditions. Sustain. Energy 5(1), 293–300 (2014)Google Scholar
  14. 14.
    Nguyen, D.D., Lehman, B., Kamarthi, S.: Performance evaluation of solar photovoltaic arrays including shadow effects using neural network. IEEE Energy Convers. Congr. Expo. 6(2), 3357–3362 (2009)Google Scholar
  15. 15.
    Nikolaou, N., Edakunni, N., Kull, M., Flach, P., Brown, G.: Cost-sensitive boosting algorithms: do we really need them? Mach. Learn. 104(2), 359–384 (2016)CrossRefMATHMathSciNetGoogle Scholar
  16. 16.
    Psarros, G., Batzelis, E., Papathanassiou, S.: Analysis of local MPPs on the P-V curve of a partially shaded PV string. In: EU PVSEC, pp. 3383–3389 (2014)Google Scholar
  17. 17.
    Psarros, G.N., Batzelis, E.I., Papathanassiou, S.A.: Partial shading analysis of multistring PV arrays and derivation of simplified MPP expressions. IEEE Trans. Sustain. Energy 6(2), 499–508 (2015)CrossRefGoogle Scholar
  18. 18.
    Rodrigo, P., Fernández, F., Almonacid, F., Pérez-Higueras, J.: A simple accurate model for the calculation of shading power losses in photovoltaic generators. Sol. Energy 93, 322–333 (2013)CrossRefGoogle Scholar
  19. 19.
    Viola, P., Jones, M.J.: Robust real-time face detection. IJCV 57(2), 137–154 (2004)CrossRefGoogle Scholar
  20. 20.
    Wyner, A.J., Olson, M., Bleich, J., Mease, D.: Explaining the success of adaboost and random forests as interpolating classifiers (2017). arXiv:1504.07676v2

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Nikolaos Nikolaou
    • 1
  • Efstratios Batzelis
    • 2
  • Gavin Brown
    • 1
  1. 1.School of Computer ScienceUniversity of ManchesterManchesterUK
  2. 2.Department of Electrical and Electronic EngineeringImperial College LondonLondonUK

Personalised recommendations