Short-term wind speed forecasting for Colonia Eulacio, Soriano Department, Uruguay, is performed by applying an artificial neural network (ANN) technique to the hourly time series representative of the site. To train the ANN and validate the technique, data for one year are collected by one tower, with anemometers installed at heights of 101.8, 81.8, 25.7, and 10.0 m. Different ANN configurations are applied for each site and height; then, a quantitative analysis is conducted, and the statistical results are evaluated to select the configuration that best predicts the real data. This method has lower computational costs than other techniques, such as numerical modelling. For integrating wind power into existing grid systems, accurate short-term wind speed forecasting is fundamental. Therefore, the proposed short-term wind speed forecasting method is an important scientific contribution for reliable large-scale wind power forecasting and integration in Uruguay. The results of the short-term wind speed forecasting showed good accuracy at all the anemometer heights tested, suggesting that the method is a powerful tool that can help the Administración Nacional de Usinas y Transmissiones Eléctricas manage the national energy supply.

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In this work is presented a statistical description of wind profile in the first 100 m of height of the Planetary Boundary Layer, taking account the measurements in the tower Colonia Eulacio Uruguay. This tower has high vertical resolution of wind velocity measurements, form 10.1 m to 101.8 m. Thermometer are installed in 3.4 m and 100.8 m, also the tower is equipped with wind vane and pyranometer. We present the diurnal cycle of mean wind, intensity of turbulence in dependence of height, also standard deviation of direction is described as a measure of turbulence in wind. Stability state is computed with vertical gradient of temperature. Before sunrise (unstable condition) is seen a decrease in mean velocity of top levels (81.8 m and 101.8 m) and increase in lower levels (10.1 m and 25.7 m). Higher dispersion in dT/dz can be seen during night time (stable condition), superadiabatic values -0.02 ◦C/m can be seen during daytime with slow dispersion. Intensity of turbulence decrease with height, for all stability conditions, is seen a increase in intensity of turbulence for unstable condition.

The conventional sources of energy such as oil, natural gas, coal, or nuclear are finite and generate environmental pollution. Alternatively, renewable energy source like wind is clean and abundantly available in nature. Wind power has a huge potential of becoming a major source of renewable energy for this modern world. It is a clean, emission-free power generation technology. Wind energy has been experiencing very rapid growth in Brazil and in Uruguay; therefore, it's a promising industry in these countries. Thus, this rapid expansion can bring several regional benefits and contribute to sustainable development, especially in places with low economic development. Therefore, the scope of this chapter is to estimate short-term wind speed forecasting applying computational intelligence, by recurrent neural networks (RNN), using anemometers data collected by an anemometric tower at a height of 100.0 m in Brazil (tropical region) and 101.8 m in Uruguay (subtropical region), both Latin American countries. The results of this study are compared with wind speed prediction results from the literature. In one of the cases investigated, this study proved to be more appropriate when analyzing evaluation metrics (error and regression) of the prediction results obtained by the proposed model.