This document describes a project using a radial basis function neural network to predict wind speed. It discusses motivations for wind speed prediction and for using neural networks. It outlines objectives to design and test an RBF network model using historical wind data. The methodology describes collecting wind data, selecting input variables, training the RBF network with different configurations, and comparing its performance to other techniques. Results show the RBF network outperformed persistence and backpropagation models with some configurations achieving more accurate 1-hour ahead predictions. The conclusion discusses findings and recommendations for further improving wind speed prediction.