Artificial neural networks applications in wind energy systems: a review Rasit Ata n Celal Bayar University, Department of Electrical & Electronic Engineering, Manisa, Turkey article info Article history: Received 30 August 2014 Received in revised form 15 April 2015 Accepted 26 April 2015 Keywords: Artificial neural networks Wind energy sytems System modeling System performance prediction abstract Neural networks approaches are becoming useful as an alternate way to classical methods. As a computation and learning paradigm, they are presented as a different modeling approach to solve complicated problems. They have been used to solve complicated practical problems in various areas, such as engineering, medicine, business, manufacturing, military etc. They have also been applied for modeling, identification, optimization, prediction, forecasting, evaluation, classification, and control of complex systems. During the last three decades, artificial neural network have been extensively employed in numerous fields of science and technology. They are not programmed in the conventional procedure but they are trained using data exemplifying the behaviour of a system. This study presents various applications of neural networks used in wind energy systems. The applications of neural networks in wind energy systems could be grouped in three major categories: forecasting and prediction, prediction and control, identification and evaluation. The main purpose of this paper is to present an overview of the neural network applications in wind energy systems. Published literature presented in this study indicate the potential of ANN as a useful tool for wind energy systems. Author strongly believes that this survey will be very much useful to the researchers, scientific engineers working in this area to find out the relevant references and current state of the field. & 2015 Elsevier Ltd. All rights reserved. Contents 1. Introduction ........................................................................................................ 535 2. Artificial neural networks ............................................................................................. 535 3. Hybrid systems ..................................................................................................... 537 3.1. Genetic algorithm(GA) and neural networks ........................................................................ 537 3.2. Particle swarm optimization(PSO) and neural networks ............................................................... 537 3.3. Wavelet neural networks ....................................................................................... 537 3.4. Fuzzy neural networks ......................................................................................... 537 4. Applications of ANNs in wind energy systems............................................................................. 537 4.1. Forecasting and prediction ...................................................................................... 538 4.1.1. Short-term wind power prediction ......................................................................... 538 4.1.2. Wind speed prediction .................................................................................. 540 4.1.3. Short-term wind speed forecasting......................................................................... 545 4.1.4. Wind power estimation.................................................................................. 546 4.1.5. Very-short term wind speed prediction ..................................................................... 548 4.1.6. Long- term wind speed prediction ......................................................................... 550 4.2. Prediction and control .......................................................................................... 551 4.2.1. Wind turbine power control .............................................................................. 551 4.2.2. Reactive power control .................................................................................. 552 4.2.3. Pitch angle control and prediction ......................................................................... 552 4.2.4. MPPT control .......................................................................................... 553 4.2.5. Voltage and frequency control ............................................................................ 554 Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/rser Renewable and Sustainable Energy Reviews http://dx.doi.org/10.1016/j.rser.2015.04.166 1364-0321/& 2015 Elsevier Ltd. All rights reserved. n Fax: þ90 2362412143. E-mail address: rasit.ata@cbu.edu.tr Renewable and Sustainable Energy Reviews 49 (2015) 534–562