Generalized adaptive neuro-fuzzy based method for wind speed distribution prediction

• Probabilistic distribution of wind speed as important wind characteristics.
• Two parameter Weibull distribution is widely used and accepted method.
• Adaptive neuro-fuzzy inference system (ANFIS) as alternative to analytical approach.
• ANFIS offers no required knowledge of internal system parameters.
• ANFIS predicts the annual wind speed probability density distribution.

The probabilistic distribution of wind speed is one of the important wind characteristics for the assessment of wind energy potential and for the performance of wind energy conversion systems. When the wind speed probability distribution is known, the wind energy distribution can easily be obtained. Therefore, the probability distribution of wind speed is a very important piece of information needed in the assessment of wind energy potential. For this reason, a large number of studies have been published concerning the use of a variety of probability density functions to describe wind speed frequency distributions. Two parameter Weibull distribution is widely used and accepted method. Artificial neural networks (ANN) can be used as an alternative to analytical approach as ANN offers advantages such as no required knowledge of internal system parameters, compact solution for multi-variable problems. In this investigation adaptive neuro-fuzzy inference system (ANFIS), which is a specific type of the ANN family, was used to predict the annual probability density distribution of wind speed. The simulation results presented in this paper show the effectiveness of the developed method.