The Prediction Model of Characteristics for Wind Turbines Based on Meteorological Properties Using Neural Network Swarm Intelligence

Tugce Demirdelen, Pırıl Tekin, Inayet Ozge Aksu and Firat Ekinci
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Tugce Demirdelen: Department of Electrical and Electronics Engineering, Adana Alparslan Turkes Science and Technology University, 01250 Adana, Turkey
Pırıl Tekin: Department of Industrial Engineering, Adana Alparslan Turkes Science and Technology University, 01250 Adana, Turkey
Inayet Ozge Aksu: Department of Computer Engineering, Adana Alparslan Turkes Science and Technology University, 01250 Adana, Turkey
Firat Ekinci: Department of Energy Systems Engineering, Adana Alparslan Turkes Science and Technology University, 01250 Adana, Turkey

Sustainability, 2019, vol. 11, issue 17, 1-18

Abstract: In order to produce more efficient, sustainable-clean energy, accurate prediction of wind turbine design parameters provide to work the system efficiency at the maximum level. For this purpose, this paper appears with the aim of obtaining the optimum prediction of the turbine parameter efficiently. Firstly, the motivation to achieve an accurate wind turbine design is presented with the analysis of three different models based on artificial neural networks comparatively given for maximum energy production. It is followed by the implementation of wind turbine model and hybrid models developed by using both neural network and optimization models. In this study, the ANN-FA hybrid structure model is firstly used and also ANN coefficients are trained by FA to give a new approach in literature for wind turbine parameters’ estimation. The main contribution of this paper is that seven important wind turbine parameters are predicted. Aiming to fill the mentioned research gap, this paper outlines combined forecasting turbine design approaches and presents wind turbine performance in detail. Furthermore, the present study also points out the possible further research directions of combined techniques so as to help researchers in the field develop more effective wind turbine design according to geographical conditions.

Keywords: optimization; wind energy; wind turbine optimized model; wind turbine parameter prediction; firefly algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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