Wind energy - generated from wind power - is plentiful, renewable, clean and available in many places in the world. This energy is generated by wind turbines, in which the wind captured by propellers is connected to a turbine that drives an electrical generator. The use of this source to generate electricity on a commercial scale began in the 1970s, when the international oil crisis escalated. The U.S. and some European countries became interested in the development of alternatives for the production of electricity sources, seeking to reduce dependence on oil and coal. The use of wind power to generate electricity has some drawbacks, however, such as uncertainties in generation and some difficulty in planning and operation of the power system. Several models for wind power forecasting using artificial neural networks have been presented with promising results. This paper presents the use of an ensemble approach to improve the results obtained by models using artificial neural networks, specifically reservoir computing. Reservoir computing is a new paradigm that offers an intuitive methodology for using the temporal processing power of RNNs without the inconvenience of training them. The main issue of using ensemble approach is the consideration of accuracy and diversity of individual predictors which constitute an ensemble.