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Pronóstico de la velocidad del viento mediante técnicas de Inteligencia Artificial

N -steps forecasting is not an easy task; it is important to develop and use techniques that simplify this task. This paper tests Artificial Intelligence models for three different multi-step forecasting strategies: Iterative, Direct, and Multiple Input Multiple Output (MIMO). The first step preprocesses the time-series to clean (remove outliers and noise) and transform the data into sequences or images; after that, three types of Artificial Neural Networks (ANN) are used to construct forecasting models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). Experiments that use data generated from climate stations in Michoacan, Mexico are shown, where the performance obtained with each model and strategy can be observed.