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Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory

Author

Listed:
  • Erick López

    (Departamento de Informática, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile)

  • Carlos Valle

    (Departamento de Informática, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile)

  • Héctor Allende

    (Departamento de Informática, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile)

  • Esteban Gil

    (Departamento de Ingeniería Eléctrica, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile)

  • Henrik Madsen

    (Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kongens Lyngby, Denmark)

Abstract

Wind power generation has presented an important development around the world. However, its integration into electrical systems presents numerous challenges due to the variable nature of the wind. Therefore, to maintain an economical and reliable electricity supply, it is necessary to accurately predict wind generation. The Wind Power Prediction Tool (WPPT) has been proposed to solve this task using the power curve associated with a wind farm. Recurrent Neural Networks (RNNs) model complex non-linear relationships without requiring explicit mathematical expressions that relate the variables involved. In particular, two types of RNN, Long Short-Term Memory (LSTM) and Echo State Network (ESN), have shown good results in time series forecasting. In this work, we present an LSTM+ESN architecture that combines the characteristics of both networks. An architecture similar to an ESN is proposed, but using LSTM blocks as units in the hidden layer. The training process of this network has two key stages: (i) the hidden layer is trained with a descending gradient method online using one epoch; (ii) the output layer is adjusted with a regularized regression. In particular, the case is proposed where Step (i) is used as a target for the input signal, in order to extract characteristics automatically as the autoencoder approach; and in the second stage (ii), a quantile regression is used in order to obtain a robust estimate of the expected target. The experimental results show that LSTM+ESN using the autoencoder and quantile regression outperforms the WPPT model in all global metrics used.

Suggested Citation

  • Erick López & Carlos Valle & Héctor Allende & Esteban Gil & Henrik Madsen, 2018. "Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory," Energies, MDPI, vol. 11(3), pages 1-22, February.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:526-:d:134018
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    References listed on IDEAS

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    7. Peng Qian & Xiange Tian & Jamil Kanfoud & Joash Lap Yan Lee & Tat-Hean Gan, 2019. "A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network," Energies, MDPI, vol. 12(18), pages 1-15, September.
    8. Wang, Cong & Zhang, Hongli & Ma, Ping, 2020. "Wind power forecasting based on singular spectrum analysis and a new hybrid Laguerre neural network," Applied Energy, Elsevier, vol. 259(C).
    9. Seyed Milad Mousavi & Majid Ghasemi & Mahsa Dehghan Manshadi & Amir Mosavi, 2021. "Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory," Mathematics, MDPI, vol. 9(8), pages 1-16, April.
    10. Ivan S. Maksymov, 2023. "Analogue and Physical Reservoir Computing Using Water Waves: Applications in Power Engineering and Beyond," Energies, MDPI, vol. 16(14), pages 1-26, July.
    11. Paweł Piotrowski & Inajara Rutyna & Dariusz Baczyński & Marcin Kopyt, 2022. "Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors," Energies, MDPI, vol. 15(24), pages 1-38, December.
    12. Mohammad Abdul Baseer & Anas Almunif & Ibrahim Alsaduni & Nazia Tazeen, 2023. "Electrical Power Generation Forecasting from Renewable Energy Systems Using Artificial Intelligence Techniques," Energies, MDPI, vol. 16(18), pages 1-21, September.
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    14. Gu, Bo & Zhang, Tianren & Meng, Hang & Zhang, Jinhua, 2021. "Short-term forecasting and uncertainty analysis of wind power based on long short-term memory, cloud model and non-parametric kernel density estimation," Renewable Energy, Elsevier, vol. 164(C), pages 687-708.
    15. Alexandru Pîrjan & George Căruțașu & Dana-Mihaela Petroșanu, 2018. "Designing, Developing, and Implementing a Forecasting Method for the Produced and Consumed Electricity in the Case of Small Wind Farms Situated on Quite Complex Hilly Terrain," Energies, MDPI, vol. 11(10), pages 1-42, October.
    16. Zhang, Jinhua & Yan, Jie & Infield, David & Liu, Yongqian & Lien, Fue-sang, 2019. "Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model," Applied Energy, Elsevier, vol. 241(C), pages 229-244.
    17. Jianzhong Zhou & Han Liu & Yanhe Xu & Wei Jiang, 2018. "A Hybrid Framework for Short Term Multi-Step Wind Speed Forecasting Based on Variational Model Decomposition and Convolutional Neural Network," Energies, MDPI, vol. 11(9), pages 1-18, August.
    18. Francisco Martínez-Álvarez & Alicia Troncoso & José C. Riquelme, 2018. "Data Science and Big Data in Energy Forecasting," Energies, MDPI, vol. 11(11), pages 1-2, November.
    19. Ahmad, Tanveer & Zhang, Dongdong, 2022. "A data-driven deep sequence-to-sequence long-short memory method along with a gated recurrent neural network for wind power forecasting," Energy, Elsevier, vol. 239(PB).
    20. Hong, Ying-Yi & Rioflorido, Christian Lian Paulo P., 2019. "A hybrid deep learning-based neural network for 24-h ahead wind power forecasting," Applied Energy, Elsevier, vol. 250(C), pages 530-539.
    21. Meka, Rajitha & Alaeddini, Adel & Bhaganagar, Kiran, 2021. "A robust deep learning framework for short-term wind power forecast of a full-scale wind farm using atmospheric variables," Energy, Elsevier, vol. 221(C).
    22. Gabriel Trierweiler Ribeiro & João Guilherme Sauer & Naylene Fraccanabbia & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2020. "Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting," Energies, MDPI, vol. 13(9), pages 1-19, May.
    23. Guangyu Qin & Qingyou Yan & Jingyao Zhu & Chuanbo Xu & Daniel M. Kammen, 2021. "Day-Ahead Wind Power Forecasting Based on Wind Load Data Using Hybrid Optimization Algorithm," Sustainability, MDPI, vol. 13(3), pages 1-17, January.
    24. Han, Li & Jing, Huitian & Zhang, Rongchang & Gao, Zhiyu, 2019. "Wind power forecast based on improved Long Short Term Memory network," Energy, Elsevier, vol. 189(C).

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