Skip to main content
SpringerLink
Log in

Gaussian Process Based Method for Point and Probabilistic Short-Term Wind Power Forecast

  • Conference paper
  • First Online:
Book cover Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.2 (SETIT 2018)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 147))

Abstract

Wind power is becoming one of the most promising renewable energy sources. With a total capacity exceeding 486 gigawatts worldwide in 2016, wind power optimization, forecast and control become more challenging than ever before. Forecasting wind turbines output for a period of time in advance is beneficial for grid managers, since it allows them to optimize their generation plans and to control the production of conventional thermal or nuclear plants. This paper proposes then a Gaussian process based method for predicting the production of a wind farm for one and two hours in advance. Both point and probabilistic forecasts are performed through customizable prediction intervals with different confidence levels. The model is tested using real data from Sidi Daoud wind farm in northeast Tunisia. Results are analyzed and compared to similar methods in terms of various assessment metrics.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Le, D., Berizzi, A., Bovo, C.: A probabilistic security assessment approach to power systems with integrated wind resources. Renew. Energy 85, 114–123 (2016)

    Article  Google Scholar 

  2. Bian, X., Geng, Y., Yuan, F., Lo, K.L., Fu, Y.: Identification and improvement of probabilistic voltage instability modes of power system with wind power integration. Electric Power Syst. Res. 140, 162–172 (2016)

    Article  Google Scholar 

  3. Xydas, E., Qadrdan, M., Marmaras, C., Cipcigan, L., Jenkins, N., Ameli, H.: Probabilistic wind power forecasting and its application in the scheduling of gas-fired generators. Appl. Energy 192, 382–394 (2017)

    Article  Google Scholar 

  4. Botterud, A., Zhou, Z., Wang, J., Sumaili, J., Keko, H., Mendes, J., Bessa, R.J., Miranda, V.: Demand dispatch and probabilistic wind power forecasting in unit commitment and economic dispatch: a case study of illinois. IEEE Trans. Sustain. Energy 4(1), 250–261 (2013)

    Article  Google Scholar 

  5. Matos, M.A., Bessa, R.J.: Setting the operating reserve using probabilistic wind power forecasts. IEEE Trans. Power Syst. 26(2), 594–603 (2011)

    Article  Google Scholar 

  6. Kou, P., Gao, F., Guan, X.: Stochastic predictive control of battery energy storage for wind farm dispatching: using probabilistic wind power forecasts. Renew. Energy 80, 286–300 (2015)

    Article  Google Scholar 

  7. Botterud, A., Zhou, Z., Wang, J., Valenzuela, J., Sumaili, J., Bessa, R.J., Keko, H., Miranda, V.: Unit commitment and operating reserves with probabilistic wind power forecasts. In: 2011 IEEE Trondheim PowerTech, pp. 1–7, June 2011

    Google Scholar 

  8. Zhang, Y., Wang, J., Wang, X.: Review on probabilistic forecasting of wind power generation. Renew. Sustain. Energy Rev. 32, 255–270 (2014)

    Article  Google Scholar 

  9. Li, P., Guan, X., Wu, J.: Aggregated wind power generation probabilistic forecasting based on particle filter. Energy Convers. Manag. 96, 579–587 (2015)

    Article  Google Scholar 

  10. Huang, C.M., Huang, Y.C., Huang, K.Y., Chen, S.J., Yang, S.P.: Deterministic and probabilistic wind power forecasting using a hybrid method. In: 2017 IEEE International Conference on Industrial Technology (ICIT), pp. 400–405, March 2017

    Google Scholar 

  11. Wan, C., Lin, J., Wang, J., Song, Y., Dong, Z.Y.: Direct quantile regression for nonparametric probabilistic forecasting of wind power generation. IEEE Trans. Power Syst. 32(4), 2767–2778 (2017)

    Article  Google Scholar 

  12. Yan, J., Li, K., Bai, E.W., Deng, J., Foley, A.M.: Hybrid probabilistic wind power forecasting using temporally local gaussian process. IEEE Trans. Sustain. Energy 7(1), 87–95 (2016)

    Article  Google Scholar 

  13. Kou, P., Liang, D., Gao, F., Gao, L.: Probabilistic wind power forecasting with online model selection and warped Gaussian process. Energy Convers. Manag. 84, 649–663 (2014)

    Article  Google Scholar 

  14. Mangalova, E., Shesterneva, O.: K-nearest neighbors for GEFCom2014 probabilistic wind power forecasting. Int. J. Forecasting 32(3), 1067–1073 (2016)

    Article  Google Scholar 

  15. Zhang, Y., Wang, J.: K-nearest neighbors and a kernel density estimator for GEFCom2014 probabilistic wind power forecasting. Int. J. Forecasting 32(3), 1074–1080 (2016)

    Article  MathSciNet  Google Scholar 

  16. Gallego-Castillo, C., Bessa, R., Cavalcante, L., Lopez-Garcia, O.: On-line quantile regression in the RKHS (reproducing kernel hilbert space) for operational probabilistic forecasting of wind power. Energy 113, 355–365 (2016)

    Article  Google Scholar 

  17. Aguilar, S., Souza, R.C., Pensanha, J.F.: Predicting probabilistic wind power generation using nonparametric techniques. In: 2014 International Conference on Renewable Energy Research and Application (ICRERA), pp. 709–712, October 2014

    Google Scholar 

  18. Wei, Z., Liu, S.M., Wei, D., Wang, Z.J., Yang, M.L., Li, Y.: Probabilistic wind power forecast using sparse Bayesian learning of unified kernel function. In: 2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), pp. 1–4, August 2014

    Google Scholar 

  19. Lee, D., Baldick, R.: Probabilistic wind power forecasting based on the Laplace distribution and golden search. In: 2016 IEEE/PES Transmission and Distribution Conference and Exposition (TD), pp. 1–5, May 2016

    Google Scholar 

  20. Sideratos, G., Hatziargyriou, N.D.: Probabilistic wind power forecasting using radial basis function neural networks. IEEE Trans. Power Syst. 27(4), 1788–1796 (2012)

    Article  Google Scholar 

  21. Zhang, Y., Wang, J., Luo, X.: Probabilistic wind power forecasting based on logarithmic transformation and boundary kernel. Energy Convers. Manag. 96, 440–451 (2015)

    Article  Google Scholar 

  22. Carpinone, A., Langella, R., Testa, A., Giorgio, M.: Very short-term probabilistic wind power forecasting based on Markov chain models. In: 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems, pp. 107–112, June 2010

    Google Scholar 

  23. Dowell, J., Pinson, P.: Very-short-term probabilistic wind power forecasts by sparse vector autoregression. IEEE Trans. Smart Grid 7(2), 763–770 (2016)

    Google Scholar 

  24. Nasraoui, K., Lakhoua, N., Amraoui, L.E.: Study and analysis of micro smart grid using the modeling language SysML. In: 2017 International Conference on Green Energy Conversion Systems (GECS), pp. 1–8, March 2017

    Google Scholar 

  25. Lakhoua, M.: Systemic analysis of a wind power station in Tunisia. J. Electr. Electron. Eng. 4, 83–88 (2011)

    Google Scholar 

  26. Do, C.B.: Gaussian Processes. Stanford University, Stanford (2007)

    Google Scholar 

  27. Meinshausen, N.: Quantile regression forests. J. Mach. Learn. Res. 7, 983–999 (2006)

    MathSciNet  MATH  Google Scholar 

  28. Weron, R.: Electricity price forecasting: a review of the state-of-the-art with a look into the future. Int. J. Forecasting 30(4), 1030–1081 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Advanced Technology and Intelligent Systems (LATIS), National Engineering School of Sousse (ENISO), University of Sousse, Sousse, Tunisia

    Ali Lahouar

Authors
  1. Ali Lahouar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Lahouar .

Editor information

Editors and Affiliations

  1. SETIT Lab, University of Sfax, Sfax, Tunisia

    Med Salim Bouhlel

  2. DIBRIS - University of Genoa, Genova, Genova, Italy

    Stefano Rovetta

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lahouar, A. (2020). Gaussian Process Based Method for Point and Probabilistic Short-Term Wind Power Forecast. In: Bouhlel, M., Rovetta, S. (eds) Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.2. SETIT 2018. Smart Innovation, Systems and Technologies, vol 147. Springer, Cham. https://doi.org/10.1007/978-3-030-21009-0_12

Download citation

Publish with us

Policies and ethics

Search

Navigation