A practical Bayesian framework for backpropagation networks
Neural Computation
A simulation study of artificial neural networks for nonlinear time-series forecasting
Computers and Operations Research
Kalman Filtering and Neural Networks
Kalman Filtering and Neural Networks
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Short-term wind power forecast based on cluster analysis and artificial neural networks
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
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Many published studies in wind power forecasting based on Neural Networks have provided performance factors based on error criteria. Based on the standard protocol for forecasting, the published results must provide improvement criteria over the persistence or references models of its same place. Persistence forecasting is the easier way of prediction in time series, but first order Wiener predictive filter is an enhancement of pure persistence model that have been adopted as the reference model for wind power forecasting. Pure enhanced persistence is simple but hard to beat in short-term prediction. This paper shows some experiments that have been performed by applying the standard protocols with Feed Forward and Recurrent Neural Networks architectures in the background of the requirements for Open Electricity Markets.