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Wind energy forecasting helps to manage power production, and hence, reducesenergy cost. Deep Neural Networks (DNN) mimics hierarchical learning in thehuman brain and thus possesses hierarchical, distributed, and multi-tasklearning capabilities. Based on aforementioned characteristics, we report DeepBelief Network (DBN) based forecast engine for wind power prediction because ofits good generalization and unsupervised pre-training attributes. The proposedDBN-WP forecast engine, which exhibits stochastic feature generationcapabilities and is composed of multiple Restricted Boltzmann Machines,generates suitable features for wind power prediction using atmosphericproperties as input. DBN-WP, due to its unsupervised pre-training of RBM layersand generalization capabilities, is able to learn the fluctuations in themeteorological properties and thus is able to perform effective mapping of thewind power. In the deep network, a regression layer is appended at the end topredict sort-term wind power. It is experimentally shown that the deep learningand unsupervised pre-training capabilities of DBN based model has comparableand in some cases better results than hybrid and complex learning techniquesproposed for wind power prediction. The proposed prediction system based onDBN, achieves mean values of RMSE, MAE and SDE as 0.124, 0.083 and 0.122,respectively. Statistical analysis of several independent executions of theproposed DBN-WP wind power prediction system demonstrates the stability of thesystem. The proposed DBN-WP architecture is easy to implement and offersgeneralization as regards the change in location of the wind farm is concerned.

Deep Belief Networks Based Feature Generation and Regression for Predicting Wind Power