Forecasting Wind Power Generation Using Deep Machine Learning and Wind Tower Data

Previsão da Geração de Energia Eólica Utilizando Aprendizagem de Máquina Profunda e Dados de Torres Anemométricas

MSc Talison Augusto Correia de Melo

SENAI CIMATEC

Course structure: The purpose of this short course is to implement a predictive model based on the Multilayer Perceptron (MLP) architecture for the generation of Wind Energy from Petrolina Station anemometric towers data available in the National Environmental Data Organization System (SONDA).

Target Audience: Undergraduate students

Language: Portuguese