Forecasting of short-term wind speed at different heights using a comparative forecasting approach

: The forecasting of wind speed with high accuracy has been a very significant obstacle to the enhancementof wind power quality, for the volatile behavior of wind speed makes forecasting difficult. In order to generate morereliable wind power and to determine the best model for different heights, wind speed needs to be predicted accurately.Recent studies show that soft computing approaches are preferred over physical methods because they can provide fastand reliable techniques to forecast short-term wind speed. In this study, a multilayer perceptron neural network and anadaptive neural fuzzy inference system are utilized to both forecast wind speed and propose the best model at heights of30, 50, and 60 m. It is obvious that various internal and external parameters for soft computing methods have paramountimportance for forecasting. In order to analyze the impact of these parameters, new wind speed data were collected froma wind farm location. Miscellaneous models were created for every wind turbine elevation by adjusting the parameters ofsoft computing methods in order to improve wind speed forecasting errors. The experimental results demonstrate thatelevation of collected wind speed data significantly affects the wind speed forecasting. Our experimental results revealthat although behavior of wind speed for every height appears identical there is no single model to predict wind speedwith the best accuracy. Therefore, every model for the soft computing methods shall be modified for every particularwind turbine height so that wind speed forecasting accuracy is improved. In this way, the approaches perform with fewererrors and models can be used to predict wind speed and power at different heights.

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