Generalized feed-forward based method for wind energy prediction

Abstract

Even though a number of new mathematical functions have been proposed for modeling wind speed probability density distributions, still the Weibull function continues to be the most commonly used model in the literature. Therefore, the parameters of this function are still widely used to obtain typical wind probability density distributions for finding the wind energy potential by researchers, engineers and designers. Once long-term average of Weibull function’s parameters are known, then the probability density distributions can easily be obtained. Artificial neural network (ANN) can be used as 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 work, a generalized feed-forward type of neural network is used to predict an annual wind speed probability density distribution by using the Weibull function’s parameters as inputs. For verifying its validity and merits, the annual wind speed probability density distribution is also predicted by using the Weibull function. The wind speed distribution predicted from the ANN modeling is compared with the analytical model’s results. Total 9year long hourly wind speed data, belonging to one of the windiest locations in Turkey with mean wind speed of over 6m/s, are used in this study. It is observed that ANN based wind speed distribution estimation gives better results for calculating the energy output from some commercial wind turbine generators.