This article presents an adaptive very short-term wind power prediction scheme that uses an artificial neural network as predictor along with adaptive Bayesian learning and Gaussian process approximation. A set of recent wind speed measurements samples composes the predictor's inputs. The predictor's parameters are adaptively optimized so that, at a given time t, its outputs approximate the future values of the generated electrical power. An evaluation of this prediction scheme was conducted for two tests cases: the predictor was set to simultaneously estimate the values of the wind power for the following prediction horizons: 5 min, 10 min and 15 min for test case n(o) 1 and for the test case n(o) 2, the prediction horizons were 10 min, 20 min and 30 min. The neural predictor performs better than the persistent model for both test cases. Moreover, the Bayesian framework also permits to predict, for a specified level of probability, the interval within which the generated power should be observed. (C) 2010 Elsevier Ltd. All rights reserved.