Wind energy plays an important role as a contributing source of energy, as well as, and in future. It has become very important to predict the speed and direction in wind farms. Effective wind prediction has always been challenged by the nonlinear and non-stationary characteristics of the wind stream. This paper presents three new models for wind speed forecasting, a day ahead, for Egyptian North-Western Mediterranean coast. These wind speed models are based on adaptive neuro-fuzzy inference system (ANFIS) estimation scheme. The first proposed model predicts wind speed for one day ahead twenty four hours based on same month of real data in seven consecutive years. The second proposed model predicts twenty four hours ahead based only one month of data using a time series predication schemes. The third proposed model is based on one month of data to predict twenty four hours ahead; the data initially passed through discrete Kalman filter (KF) for the purpose of minimizing the noise contents that resulted from the uncertainties encountered during the wind speed measurement. Kalman filtered data manipulated by the third model showed better estimation results over the other two models, and decreased the mean absolute percentage error by approximately 64 % over the first model.