From year to year, the number of new installations producing electricity from renewable energy sources increases. This phenomenon, to varying degrees, applies to all types of renewable resources, though the fastest growing is wind power. Connecting new wind farms to the power system necessitates solving the problems related to the work of these energy sources. The most important issues include the large variation in the level of power generated and the difficulty in predicting changes in the supply. For years, attempts have been made to reduce the negative factors, initially focusing on preparing new forecasting tools dedicated to wind farms. Also observable has been the acceleration of work associated with the development of the technology and the construction of electrical energy storage capacity. The objective of this study is the combination of these two trends and the development of two applicable tools. The first model will provide the opportunity to prepare the best possible forecast of power generation. The second will be a tool for defining the optimum storage capacity, inter alia, on the basis of information obtained fromthe forecastingmodel. This paper presents the activities related to the implementation of the first tool. It describes the process of filtering the measured data and the problems associated with it, and presents the concept of a predictive model. The learning phase of the model is outlined together with creating a forecast for power levels. Also shown is the accuracy of the forecasts, and formulation for the successive stages of work.