Short-term wind speed forecasting in Uruguay using computational intelligence

Abstract

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.