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A Combined Fuzzy GMDH Neural Network and Grey Wolf Optimization Application for Wind Turbine Power Production Forecasting Considering SCADA Data

Author

Listed:
  • Azim Heydari

    (Department of Astronautical, Electrical and Energy Engineering (DIAEE), Sapienza University, 00184 Rome, Italy)

  • Meysam Majidi Nezhad

    (Department of Astronautical, Electrical and Energy Engineering (DIAEE), Sapienza University, 00184 Rome, Italy)

  • Mehdi Neshat

    (Optimization and Logistics Group, School of Computer Science, University of Adelaide, Adelaide 5005, Australia)

  • Davide Astiaso Garcia

    (Department of Planning, Design, and Technology of Architecture, Sapienza University of Rome, 00197 Rome, Italy)

  • Farshid Keynia

    (Department of Energy Management and Optimization, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman 7631133131, Iran)

  • Livio De Santoli

    (Department of Astronautical, Electrical and Energy Engineering (DIAEE), Sapienza University, 00184 Rome, Italy)

  • Lina Bertling Tjernberg

    (School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology Stockholm, 10044 Stockholm, Sweden)

Abstract

A cost-effective and efficient wind energy production trend leads to larger wind turbine generators and drive for more advanced forecast models to increase their accuracy. This paper proposes a combined forecasting model that consists of empirical mode decomposition, fuzzy group method of data handling neural network, and grey wolf optimization algorithm. A combined K-means and identifying density-based local outliers is applied to detect and clean the outliers of the raw supervisory control and data acquisition data in the proposed forecasting model. Moreover, the empirical mode decomposition is employed to decompose signals and pre-processing data. The fuzzy GMDH neural network is a forecaster engine to estimate the future amount of wind turbines energy production, where the grey wolf optimization is used to optimize the fuzzy GMDH neural network parameters in order to achieve a lower forecasting error. Moreover, the model has been applied using actual data from a pilot onshore wind farm in Sweden. The obtained results indicate that the proposed model has a higher accuracy than others in the literature and provides single and combined forecasting models in different time-steps ahead and seasons.

Suggested Citation

  • Azim Heydari & Meysam Majidi Nezhad & Mehdi Neshat & Davide Astiaso Garcia & Farshid Keynia & Livio De Santoli & Lina Bertling Tjernberg, 2021. "A Combined Fuzzy GMDH Neural Network and Grey Wolf Optimization Application for Wind Turbine Power Production Forecasting Considering SCADA Data," Energies, MDPI, vol. 14(12), pages 1-13, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3459-:d:573236
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    References listed on IDEAS

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    Cited by:

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