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Energies
Volume 14
Issue 12
10.3390/en14123459
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Article

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

by
Azim Heydari
1,*,
Meysam Majidi Nezhad
1,
Mehdi Neshat
2,
Davide Astiaso Garcia
3,
Farshid Keynia
4,
Livio De Santoli
1 and
Lina Bertling Tjernberg
5
1
Department of Astronautical, Electrical and Energy Engineering (DIAEE), Sapienza University, 00184 Rome, Italy
2
Optimization and Logistics Group, School of Computer Science, University of Adelaide, Adelaide 5005, Australia
3
Department of Planning, Design, and Technology of Architecture, Sapienza University of Rome, 00197 Rome, Italy
4
Department of Energy Management and Optimization, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman 7631133131, Iran
5
School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology Stockholm, 10044 Stockholm, Sweden
*
Author to whom correspondence should be addressed.
Academic Editor: Francesco Castellani
Energies 2021, 14(12), 3459; https://doi.org/10.3390/en14123459
Received: 7 May 2021 / Revised: 2 June 2021 / Accepted: 9 June 2021 / Published: 11 June 2021
(This article belongs to the Special Issue GIS and Remote Sensing for Renewable Energy Assessment and Maps)
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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. View Full-Text
Keywords: power system; wind power production; SCADA data; fuzzy GMDH neural network; grey wolf optimization power system; wind power production; SCADA data; fuzzy GMDH neural network; grey wolf optimization
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MDPI and ACS Style

Heydari, A.; Majidi Nezhad, M.; Neshat, M.; Garcia, D.A.; Keynia, F.; De Santoli, L.; Bertling Tjernberg, L. A Combined Fuzzy GMDH Neural Network and Grey Wolf Optimization Application for Wind Turbine Power Production Forecasting Considering SCADA Data. Energies 2021, 14, 3459. https://doi.org/10.3390/en14123459

AMA Style

Heydari A, Majidi Nezhad M, Neshat M, Garcia DA, Keynia F, De Santoli L, Bertling Tjernberg L. A Combined Fuzzy GMDH Neural Network and Grey Wolf Optimization Application for Wind Turbine Power Production Forecasting Considering SCADA Data. Energies. 2021; 14(12):3459. https://doi.org/10.3390/en14123459

Chicago/Turabian Style

Heydari, Azim, Meysam Majidi Nezhad, Mehdi Neshat, Davide A. Garcia, Farshid Keynia, Livio De Santoli, and 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 14, no. 12: 3459. https://doi.org/10.3390/en14123459

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