Early power loss detection in wind turbines is a key for the wind energy industry to avoid elevated maintenance costs and reduce the uncertainty regarding generated power estimations. Location, especially of those wind farms isolated offshore, causes the strategy of scheduled-only maintenance inefficient and very costly, additionally presenting a typically long downtime after a breakdown. These problems point to the creation of predictive solutions to anticipate the maintenance procedure, preparing the necessary parts and avoiding the possibility of destructive failures. Predicting failures in structures of such complexity requires modeling their multiple components individually in addition to the whole system. For this purpose, physics-based and data-driven models are used, which have proven themselves in this context. Machine learning has proven to be a valuable resource for solving a variety of problems in this industry. Thus, we will propose data-driven Deep Learning methods to compute the Power output of wind turbines with respect to all the mechanical and electrical features by using two types of Deep Neural Networks: a simpler combination of linear layers and a Long-Short Term Memory Neural Network. Then, with the use of a one-dimensional Convolutional Neural Network we will predict the time to failure of the system.