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Wind Energy Forecasting with Artificial Intelligence Techniques: A Review

  • Jorge Maldonado-CorreaEmail author
  • Marcelo Valdiviezo
  • Juan Solano
  • Marco Rojas
  • Carlos Samaniego-Ojeda
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1194)

Abstract

The World Wind Energy Association (WWEA) forecasts that installed wind capacity worldwide will reach 800 GW by the end of 2021. Because wind is a random resource, both in speed and direction, the short-term forecasting of wind energy has become an important issue to be investigated. In this paper, a Systematic Literature Review (SLR) on non-parametric models and techniques for predicting short-term wind energy is presented based on four research questions related to both already applied methodologies and wind physical variables in order to determine the state of the art for the development of the research project “Artificial intelligence system for the short-term prediction of the energy production of the Villonaco wind farm”. The results indicate that artificial neural networks (ANN) and support-vector machines (SVMs) were mainly used in related studies. In addition, ANNs are highlighted in comparison with other techniques of Wind Energy Forecasting.

Keywords

Forecasting wind energy Artificial intelligence Wind farm 

Notes

Acknowledgement

The authors acknowledge the support of the ‘Universidad Nacional de Loja’ by means of the research project: Artificial intelligence system for the short-term prediction of the energy production of the Villonaco wind farm. 26-DI-FEIRNNR-2019.

References

  1. 1.
    Wang, H., Wang, H., Jiang, G., Li, J., Wang, Y.: Early fault detection of wind turbines based on operational condition clustering and optimized deep belief network modeling. Energies 12(6), 984 (2019)CrossRefGoogle Scholar
  2. 2.
    Pandit, R., Infield, D.: Gaussian process operational curves for wind turbine condition monitoring. Energies 11(7), 1631 (2018)CrossRefGoogle Scholar
  3. 3.
    Marugán, A.P., Márquez, F.P.G., Perez, J.M.P., Ruiz-Hernández, D.: A survey of artificial neural network in wind energy systems. Appl. Energy 228, 1822–1836 (2018)CrossRefGoogle Scholar
  4. 4.
    Torres-Carrión, P.V., González-González, C.S., Aciar, S., Rodríguez-Morales, G.: Methodology for systematic literature review applied to engineering and education. In: 2018 IEEE Global Engineering Education Conference (EDUCON), pp. 1364–1373 (2018)Google Scholar
  5. 5.
    Tasnim, S., Rahman, A., Oo, A.M.T., Haque, M.E.: Wind power prediction in new stations based on knowledge of existing stations: a cluster based multi source domain adaptation approach. Knowl.-Based Syst. 145, 15–24 (2018)CrossRefGoogle Scholar
  6. 6.
    Dong, W., Yang, Q.: Ultra-short term prediction model of wind power generation based on hybrid intelligent method. In: Chinese Control Conference, CCC, pp. 9148–9153 (July 2018)Google Scholar
  7. 7.
    Lu, H., Heng, J., Wang, C.: An AI-based hybrid forecasting model for wind speed forecasting. Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS, vol. 10637, pp. 221–230. University of Technology Sydney, Sydney (2017).  https://doi.org/10.1007/978-3-319-70093-9_23
  8. 8.
    Hernandez, W., et al.: Modeling of a robust confidence band for the power curve of a wind turbine. Sensors 16(12), 2080 (2016)CrossRefGoogle Scholar
  9. 9.
    Kitchenham, B.: Procedures for performing systematic reviews. Keele 33(2004), 1–26 (2004). Joint Technical ReportGoogle Scholar
  10. 10.
    Bacca, J., Baldiris, S., Fabregat, R., Graf, S., Kinshuk: Augmented reality trends in education: a systematic review of research and applications. Educ. Technol. Soc. 17(4), 133–149 (2014)Google Scholar
  11. 11.
    Pliego Marugán, A., García Márquez, F.P.: Advanced analytics for detection and diagnosis of false alarms and faults: a real case study. Wind Energy 22(11), 1622–1635 (2019)CrossRefGoogle Scholar
  12. 12.
    Zheng, D., Shi, M., Wang, Y., Eseye, A.T., Zhang, J.: Day-ahead wind power forecasting using a two-stage hybrid modeling approach based on scada and meteorological information, and evaluating the impact of input-data dependency on forecasting accuracy. Energies 10(12), 1988 (2017)CrossRefGoogle Scholar
  13. 13.
    Velásquez, J.: Una Guía Corta para Escribir Revisiones Sistemáticas de Literatura. DYNA 82(189), 9–12 (2014)CrossRefGoogle Scholar
  14. 14.
    Cao, Y., Hu, Q., Shi, H., Zhang, Y.: Prediction of wind power generation base on neural network in consideration of the fault time. IEEJ Trans. Electr. Electron. Eng. 14(5), 670–679 (2019). Shanghai Electric Power Design Institute Co., Ltd., No. 550, Xujiahui Road, 23rd Floor, Huangpu District, Shanghai, 200090, ChinaCrossRefGoogle Scholar
  15. 15.
    Chen, Y., Pi, D.: Novel fruit fly algorithm for global optimisation and its application to short-term wind forecasting. Connect. Sci. 31(3), 244–266 (2019)CrossRefGoogle Scholar
  16. 16.
    Kazak, J., van Hoof, J., Szewranski, S.: Challenges in the wind turbines location process in Central Europe – the use of spatial decision support systems. Renew. Sustain. Energy Rev. 76, 425–433 (2017)CrossRefGoogle Scholar
  17. 17.
    Dolara, A., Gandelli, A., Grimaccia, F., Leva, S., Mussetta, M.: Weather-based machine learning technique for day-ahead wind power forecasting. In: 2017 6th International Conference on Renewable Energy Research and Applications, ICRERA 2017, pp. 206–209 (January 2017)Google Scholar
  18. 18.
    Blanchard, T., Samanta, B.: Wind speed forecasting using neural networks. Wind Eng. 44(1), 33–48 (2020).  https://doi.org/10.1177/0309524X19849846
  19. 19.
    Li, N., He, F., Ma, W.: Wind power prediction based on extreme learning machine with kernel mean p-power error loss. Energies 12(4), 673 (2019)CrossRefGoogle Scholar
  20. 20.
    Cornejo-Bueno, L., Cuadra, L., Jiménez-Fernández, S., Acevedo-Rodríguez, J., Prieto, L., Salcedo-Sanz, S.: Wind power ramp events prediction with hybrid machine learning regression techniques and reanalysis data. Energies 10(11), 1784 (2017)CrossRefGoogle Scholar
  21. 21.
    Martín-Vázquez, R., Aler, R., Galván, I.M.: Wind energy forecasting at different time horizons with individual and global models. In: Iliadis, L., Maglogiannis, I., Plagianakos, V. (eds.) AIAI 2018. IAICT, vol. 519, pp. 240–248. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-92007-8_21CrossRefGoogle Scholar
  22. 22.
    Martín-Vázquez, R., Aler, R., Galván, I.M.: A study on feature selection methods for wind energy prediction. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2017. LNCS, vol. 10305, pp. 698–707. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-59153-7_60CrossRefGoogle Scholar
  23. 23.
    Qiu, X., Ren, Y., Suganthan, P.N., Amaratunga, G.A.: Short-term wind power ramp forecasting with empirical mode decomposition based ensemble learning techniques. In: 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, pp. 1–8 (January 2018)Google Scholar
  24. 24.
    Pan, C., Tan, Q., Qin, B.: A new method of wind speed prediction based on weighted optimal fuzzy c-means and modular extreme learning machine. Wind Eng. 42(5), 447–457 (2018)CrossRefGoogle Scholar
  25. 25.
    Tahir, M., El-Shatshat, R., Salama, M.M.A.: Improved stacked ensemble based model for very short-term wind power forecasting. In: Proceedings - 2018 53rd International Universities Power Engineering Conference, UPEC 2018 (2018)Google Scholar
  26. 26.
    Cocchi, G., Galli, L., Galvan, G., Sciandrone, M., Cantù, M., Tomaselli, G.: Machine learning methods for short-term bid forecasting in the renewable energy market: a case study in Italy. Wind Energy 21(5), 357–371 (2018)CrossRefGoogle Scholar
  27. 27.
    Zaunseder, E., Müller, L., Blankenburg, S.: High accuracy forecasting with limited input data: using FFNNs to predict offshore wind power generation. In: ACM International Conference Proceeding Series, pp. 61–68 (2018)Google Scholar
  28. 28.
    Lahouar, A., Slama, J.B.H.: Hour-ahead wind power forecast based on random forests. Renew. Energy 109, 529–541 (2017)CrossRefGoogle Scholar
  29. 29.
    Salfate, I., et al.: 24-hours wind speed forecasting and wind power generation in La Serena (Chile). Wind Eng. 42(6), 607–623 (2018)CrossRefGoogle Scholar
  30. 30.
    Jiao, R., Huang, X., Ma, X., Han, L., Tian, W.: A model combining stacked auto encoder and back propagation algorithm for short-term wind power forecasting. IEEE Access 6, 17851–17858 (2018)CrossRefGoogle Scholar
  31. 31.
    Wang, Y., Hu, Q., Meng, D., Zhu, P.: Deterministic and probabilistic wind power forecasting using a variational Bayesian-based adaptive robust multi-kernel regression model. Appl. Energy 208, 1097–1112 (2017)CrossRefGoogle Scholar
  32. 32.
    Zameer, A., Arshad, J., Khan, A., Raja, M.A.Z.: Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks. Energy Convers. Manag. 134, 361–372 (2017)CrossRefGoogle Scholar
  33. 33.
    Fischer, A., Montuelle, L., Mougeot, M., Picard, D.: Statistical learning for wind power: a modeling and stability study towards forecasting. Wind Energy 20(12), 2037–2047 (2017)CrossRefGoogle Scholar
  34. 34.
    Woon, W.L., Oehmcke, S., Kramer, O.: Spatio-temporal wind power prediction using recurrent neural networks. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.-S.M. (eds.) ICONIP 2017. LNCS, vol. 10638, pp. 556–563. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-70139-4_56CrossRefGoogle Scholar
  35. 35.
    Kumar, A., Ali, A.B.M.S.: Prospects of wind energy production in the western Fiji-an empirical study using machine learning forecasting algorithms. In: 2017 Australasian Universities Power Engineering Conference, AUPEC 2017, pp. 1–5 (November 2017)Google Scholar
  36. 36.
    Prasetyowati, A., Sudibyo, H., Sudiana, D.: Wind power prediction by using wavelet decomposition mode based NARX-neural network. In: ACM International Conference Proceeding Series, pp. 275–278 (2017)Google Scholar
  37. 37.
    Díaz-Vico, D., Torres-Barrán, A., Omari, A., Dorronsoro, J.R.: Deep neural networks for wind and solar energy prediction. Neural Process. Lett. 46(3), 829–844 (2017)CrossRefGoogle Scholar
  38. 38.
    Alonzo, B., Plougonven, R., Mougeot, M., Fischer, A., Dupré, A., Drobinski, P.: From numerical weather prediction outputs to accurate local surface wind speed: statistical modeling and forecasts. In: Drobinski, P., Mougeot, M., Picard, D., Plougonven, R., Tankov, P. (eds.) FRM 2017. SPMS, vol. 254, pp. 23–44. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-99052-1_2CrossRefGoogle Scholar
  39. 39.
    Yang, J.: A novel short-term multi-input–multi-output prediction model of wind speed and wind power with LSSVM based on improved ant colony algorithm optimization. Cluster Comput. 22(2), 3293–3300 (2019). Electrical Engineering College, Northwest Minzu University, Lanzhou, 730100, ChinaCrossRefGoogle Scholar
  40. 40.
    Dong, W., Yang, Q., Fang, X.: Multi-step ahead wind power generation prediction based on hybrid machine learning techniques. Energies 11(8), 1975 (2018)CrossRefGoogle Scholar
  41. 41.
    Deo, R.C., Ghorbani, M.A., Samadianfard, S., Maraseni, T., Bilgili, M., Biazar, M.: Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data. Renew. Energy 116, 309–323 (2018)CrossRefGoogle Scholar
  42. 42.
    Browell, J., Gilbert, C., McMillan, D.: Use of turbine-level data for improved wind power forecasting. In: 2017 IEEE Manchester PowerTech, Powertech 2017 (2017)Google Scholar
  43. 43.
    Gensler, A., Sick, B.: Probabilistic wind power forecasting: A multi-scheme ensemble technique with gradual coopetitive soft gating. In: 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, pp. 1–10 (January 2018)Google Scholar
  44. 44.
    Díaz, S., Carta, J.A., Matías, J.M.: Performance assessment of five MCP models proposed for the estimation of long-term wind turbine power outputs at a target site using three machine learning techniques. Appl. Energy 209, 455–477 (2018)CrossRefGoogle Scholar
  45. 45.
    Labati, R.D., Genovese, A., Piuri, V., Scotti, F., Sforza, G.: A decision support system for wind power production. IEEE Trans. Syst. Man Cybern. Syst. 50(1), 290–304 (2018). Department of Computer Science, Università degli Studi di Milano, 26013 Crema, Italy (e-mail: ruggero.donida@unimi.it)CrossRefGoogle Scholar
  46. 46.
    Banerjee, A., Tian, J., Wang, S., Gao, W.: Weighted evaluation of wind power forecasting models using evolutionary optimization algorithms. Procedia Comput. Sci. 114, 357–365 (2017)CrossRefGoogle Scholar
  47. 47.
    Reyes, A., Ibargüengoytia, P.H., Jijón, J.D., Guerrero, T., García, U.A., Borunda, M.: Wind power forecasting for the Villonaco wind farm using AI techniques. In: Pichardo-Lagunas, O., Miranda-Jiménez, S. (eds.) MICAI 2016. LNCS (LNAI), vol. 10062, pp. 226–236. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-62428-0_19CrossRefGoogle Scholar
  48. 48.
    Zhu, Y., Chen, S., Luo, J., Wang, Y.: A novel wind power prediction technique based on radial basis function neural network. In: Qiao, F., Patnaik, S., Wang, J. (eds.) ICMIR 2017. AISC, vol. 690, pp. 180–184. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-65978-7_27CrossRefGoogle Scholar
  49. 49.
    Gonzalez, E., Stephen, B., Infield, D., Melero, J.J.: On the use of high-frequency SCADA data for improved wind turbine performance monitoring. J. Phys: Conf. Ser. 926(1), 012009 (2017)Google Scholar
  50. 50.
    Bi, R., Zhou, C., Hepburn, D.M.: Applying instantaneous SCADA data to artificial intelligence based power curve monitoring and WTG fault forecasting. In: 2016 International Conference on Smart Grid and Clean Energy Technologies, ICSGCE 2016, pp. 176–181 (2017)Google Scholar
  51. 51.
    Vidal, Y., Pozo, F., Tutivén, C.: Wind turbine multi-fault detection and classification based on SCADA data. Energies 11(11), 3018 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Universidad Nacional de Loja, Facultad de Energía, Ciudad Universitaria Guillermo FalconíLojaEcuador

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