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Application of Augmented Echo State Networks and Genetic Algorithm to Improve Short-Term Wind Speed Forecasting

Author

Listed:
  • Hugo T. V. Gouveia

    (Department of Electrical Engineering, Federal University of Pernambuco, Recife 50670-901, Brazil)

  • Murilo A. Souza

    (Department of Electrical Engineering, Federal University of Pernambuco, Recife 50670-901, Brazil)

  • Aida A. Ferreira

    (Department of Electrical Systems, Federal Institute of Pernambuco, Recife 50740-545, Brazil)

  • Jonata C. de Albuquerque

    (Department of Electrical Engineering, Federal University of Pernambuco, Recife 50670-901, Brazil)

  • Otoni Nóbrega Neto

    (Department of Electrical Engineering, Federal University of Pernambuco, Recife 50670-901, Brazil)

  • Milde Maria da Silva Lira

    (Department of Electrical Engineering, Federal University of Pernambuco, Recife 50670-901, Brazil)

  • Ronaldo R. B. de Aquino

    (Department of Electrical Engineering, Federal University of Pernambuco, Recife 50670-901, Brazil)

Abstract

The large-scale integration into electrical systems of intermittent power-generation sources, such as wind power plants, requires greater efforts and knowledge from operators to keep these systems operating efficiently. These sources require reliable output power forecasts to set up the optimal operating point of the electrical system. In previous research, the authors developed an evolutionary approach algorithm called RCDESIGN to optimize the hyperparameters and topology of Echo State Networks (ESN), and applied the model in different time series forecasting, including wind speed. In this paper, RCDESIGN was modified in some aspects of the genetic algorithm, and now it optimizes an ESN with augmented states (ESN-AS) and has been called RCDESIGN-AS. The evolutionary algorithm allows the search for the best parameters and topology of the recurrent neural network to be performed simultaneously. In addition, RCDESIGN-AS has the important characteristic of requiring little computational effort and processing time since it is not necessary for the eigenvalues of the reservoir weight matrix to be reduced and also due to the fact that the augmented states make it possible to reduce the number of neurons in the reservoir. The method was applied for wind speed forecasting with a 24-h ahead horizon using real data of wind speed from five cities in the Northeast Region of Brazil. All results obtained with the proposed method overcame forecasting performed by the persistence method, obtaining prediction gains ranging from 60% to 80% in relation to this reference method. In some datasets, the proposed method also yielded better results than the traditional ESN, showing that RCDESIGN-AS can be a powerful tool for wind-speed forecasting and possibly for other types of time series.

Suggested Citation

  • Hugo T. V. Gouveia & Murilo A. Souza & Aida A. Ferreira & Jonata C. de Albuquerque & Otoni Nóbrega Neto & Milde Maria da Silva Lira & Ronaldo R. B. de Aquino, 2023. "Application of Augmented Echo State Networks and Genetic Algorithm to Improve Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 16(6), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2635-:d:1094088
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    References listed on IDEAS

    as
    1. Athraa Ali Kadhem & Noor Izzri Abdul Wahab & Ishak Aris & Jasronita Jasni & Ahmed N. Abdalla, 2017. "Advanced Wind Speed Prediction Model Based on a Combination of Weibull Distribution and an Artificial Neural Network," Energies, MDPI, vol. 10(11), pages 1-17, October.
    2. Shen, Zhiwei & Ritter, Matthias, 2016. "Forecasting volatility of wind power production," Applied Energy, Elsevier, vol. 176(C), pages 295-308.
    3. Dehua Zheng & Min Shi & Yifeng Wang & Abinet Tesfaye Eseye & Jianhua Zhang, 2017. "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, MDPI, vol. 10(12), pages 1-23, December.
    4. Lei, Ma & Shiyan, Luan & Chuanwen, Jiang & Hongling, Liu & Yan, Zhang, 2009. "A review on the forecasting of wind speed and generated power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 915-920, May.
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