The forecasting of wind speed with high accuracy has been a very significant obstacle to the enhancement of wind power quality, for the volatile behavior of wind speed makes forecasting difficult. In order to generate more reliable 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 fast and reliable techniques to forecast short-term wind speed. In this study, a multilayer perceptron neural network and an adaptive neural fuzzy inference system are utilized to both forecast wind speed and propose the best model at heights of 30, 50, and 60 m. It is obvious that various internal and external parameters for soft computing methods have paramount importance for forecasting. In order to analyze the impact of these parameters, new wind speed data were collected from a wind farm location. Miscellaneous models were created for every wind turbine elevation by adjusting the parameters of soft computing methods in order to improve wind speed forecasting errors. The experimental results demonstrate that elevation of collected wind speed data significantly affects the wind speed forecasting. Our experimental results reveal that although behavior of wind speed for every height appears identical there is no single model to predict wind speed with the best accuracy. Therefore, every model for the soft computing methods shall be modified for every particular wind turbine height so that wind speed forecasting accuracy is improved. In this way, the approaches perform with fewer errors and models can be used to predict wind speed and power at different heights.