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

Azim Heydari, Meysam Majidi Nezhad, Mehdi Neshat, Davide Astiaso Garcia, Farshid Keynia, Livio De Santoli and Lina Bertling Tjernberg
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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

Energies, 2021, vol. 14, issue 12, 1-13

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.

Keywords: power system; wind power production; SCADA data; fuzzy GMDH neural network; grey wolf optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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