Very short-term wind power forecasting with neural networks and adaptive Bayesian learning

Ruddy Blonbou

Renewable Energy, 2011, vol. 36, issue 3, 1118-1124

Abstract: 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: 5min, 10min and 15min for test case n°1 and for the test case n°2, the prediction horizons were 10min, 20min and 30min. 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.

Keywords: Wind power prediction; Bayesian neural networks; Adaptive learning (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (25)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:36:y:2011:i:3:p:1118-1124

DOI: 10.1016/j.renene.2010.08.026

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