Influence of Deep Learning on Precision Improvement in Predictive Models of Wind Power Generation

This paper, proposes the use of Deep Learning in predictive nonparametric models that use artificial intelligence tools to approximate power curves of wind farms. Three different tools are evaluated: artificial neural networks, fuzzy inference systems and Auto Encoders, an initial model of deep learning networks. The tools are inserted in a non-parametric model of power prediction, where they are compared. The results show that the autoencoder-based power curve performs well above other proposed tools. This significantly improves the performance of the predictive power model.