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