Wind Power Forecasting Using Advanced Neural Networks Models

Georges Kariniotakis (1) , Georges Stavrakakis (2) , Eric Nogaret (1)

1 CEP/Sophia
2 Technical University of Crete [Chania]

In this paper, an advanced model, based on recurrent high order neural networks, is developed for the prediction of the power output profile of a wind park. This model outperforms simple methods like persistence, as well as classical methods in the literature. The architecture of a forecasting model is optimised automatically by a new algorithm, that substitutes the usually applied trial-and-error method. Finally, the online implementation of the developed model into an advanced control system for the optimal operation and management of a real autonomous wind-diesel power system, is presented.

Mots clés: Wind energy; Wind forecasting; Neural networks; Predictive models; Power system modeling; Power system management; Recurrent neural networks; Optimization methods; Control system synthesis; Artificial Intelligence; Renewable energies

Domaines: Energétique

Georges Kariniotakis, Georges Stavrakakis, Eric Nogaret. Wind Power Forecasting Using Advanced Neural Networks Models. IEEE Transactions on Energy Conversion, 1996, 11 (4), pp.762 - 767. ⟨10.1109/60.556376⟩. ⟨hal-01441822⟩