Nowadays, it is highly important to predict the wind speed/direction (at any wind farm). In order to determine the demanded wind power that could be captured by the available wind turbines at the farm. Effective wind prediction has always been challenged by the nonlinear and non-stationary characteristics of the wind stream. This paper presents two 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 twenty four hours ahead based only one month of data using time series predication schemes. The second proposed model is based on the same data; but 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 second model showed better estimation results over the other model, and decreased the mean absolute percentage error by approximately 51.45 % over the first model.