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Robust estimation of wind power ramp events with reservoir computing

Author

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  • Dorado-Moreno, M.
  • Cornejo-Bueno, L.
  • Gutiérrez, P.A.
  • Prieto, L.
  • Hervás-Martínez, C.
  • Salcedo-Sanz, S.

Abstract

Wind power ramp events are sudden increases or decreases of wind speed within a short period of time. Their prediction is nowadays one of the most important research trends in wind energy production because they can potentially damage wind turbines, causing an increase in wind farms management costs. In this paper, 6-h and 24-h binary (ramp/non-ramp) prediction based on reservoir computing methodology is proposed. This forecasting may be used to avoid damages in the turbines. Reservoir computing models are used because they are able to exploit the temporal structure of data. We focus on echo state networks, which are one of the most successfully applied reservoir computing models. The variables considered include past values of the ramp function and a set of meteorological variables, obtained from reanalysis data. Simulations of the system are performed in data from three wind farms located in Spain. The results show that our algorithm proposal is able to correctly predict about 60% of ramp events in both 6-h and 24-h prediction cases and 75% of the non-ramp events in the next 24-h case. These results are compared against state of the art models, obtaining in all cases significant improvements in favour of the proposed algorithm.

Suggested Citation

  • Dorado-Moreno, M. & Cornejo-Bueno, L. & Gutiérrez, P.A. & Prieto, L. & Hervás-Martínez, C. & Salcedo-Sanz, S., 2017. "Robust estimation of wind power ramp events with reservoir computing," Renewable Energy, Elsevier, vol. 111(C), pages 428-437.
    • Handle: RePEc:eee:renene:v:111:y:2017:i:c:p:428-437
    DOI: 10.1016/j.renene.2017.04.016
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    Citations

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    Cited by:

    1. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    2. Lee, Joseph C.Y. & Draxl, Caroline & Berg, Larry K., 2022. "Evaluating wind speed and power forecasts for wind energy applications using an open-source and systematic validation framework," Renewable Energy, Elsevier, vol. 200(C), pages 457-475.
    3. Laura Cornejo-Bueno & Lucas Cuadra & Silvia Jiménez-Fernández & Javier Acevedo-Rodríguez & Luis Prieto & Sancho Salcedo-Sanz, 2017. "Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data," Energies, MDPI, vol. 10(11), pages 1-27, November.
    4. EunJi Ahn & Jin Hur, 2022. "A Practical Metric to Evaluate the Ramp Events of Wind Generating Resources to Enhance the Security of Smart Energy Systems," Energies, MDPI, vol. 15(7), pages 1-16, April.
    5. Antonio Manuel Gómez-Orellana & Juan Carlos Fernández & Manuel Dorado-Moreno & Pedro Antonio Gutiérrez & César Hervás-Martínez, 2021. "Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux," Energies, MDPI, vol. 14(2), pages 1-33, January.
    6. Guglielmo D’Amico & Filippo Petroni & Salvatore Vergine, 2022. "Ramp Rate Limitation of Wind Power: An Overview," Energies, MDPI, vol. 15(16), pages 1-15, August.
    7. Wang, Lin & Lv, Sheng-Xiang & Zeng, Yu-Rong, 2018. "Effective sparse adaboost method with ESN and FOA for industrial electricity consumption forecasting in China," Energy, Elsevier, vol. 155(C), pages 1013-1031.
    8. Wang, Yun & Hu, Qinghua & Meng, Deyu & Zhu, Pengfei, 2017. "Deterministic and probabilistic wind power forecasting using a variational Bayesian-based adaptive robust multi-kernel regression model," Applied Energy, Elsevier, vol. 208(C), pages 1097-1112.
    9. Yu, Min & Niu, Dongxiao & Gao, Tian & Wang, Keke & Sun, Lijie & Li, Mingyu & Xu, Xiaomin, 2023. "A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism," Energy, Elsevier, vol. 269(C).

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