One of the most important renewable energy sources today is wind power generation.However, this energy source depends on the flow of air in the area where the wind generators are installed, and as with many other renewable resources, this is a natural resource whose magnitude is not directly controlled by man.The prediction of wind speed then becomes a key problem when we want to project the energy performance of a wind farm.The behaviour of the wind, as a climatic variable, can be estimated from various atmospheric parameters such as temperature, humidity and air pressure.In this paper we propose a multivariate wind speed estimation model from the history of these atmospheric parameters using a deep neural network.The performance of the model is then evaluated against the same historical data, which produces a fairly small error.The code is implemented in Keras deep learning library with TensorFlow backend.