Predicting long-term wind speed in wind farms of northeast Brazil: A comparative analysis through machine learning models - de Paula, M. [UNESP]; Colnago, M. [UNESP]; Fidalgo, J.; Casaca, W. [UNESP] (Institute of Electrical and Electronics Engineers (IEEE), 2020)
The rapid growth of wind generation in northeast Brazil has led to multiple benefits to many different stakeholders of energy industry, especially because the wind is a renewable resource - an abundant and ubiquitous power source present in almost every state in the northeast region of Brazil. Despite the several benefits of wind power, forecasting the wind speed becomes a challenging task in practice, as it is highly volatile over time, especially when one has to deal with long-term predictions. Therefore, this paper focuses on applying different Machine Learning strategies such as Random Forest, Neural Networks and Gradient Boosting to perform regression on wind data for long periods of time. Three wind farms in the northeast Brazil have been investigated, whose data sets were constructed from the wind farms data collections and the National Institute of Meteorology (INMET). Statistical analyses of the wind data and the optimization of the trained predictors were conducted, as well as several quantitative assessments of the obtained forecast results.
Keywords: Long-Term; Machine learning; Northeastern Brazil; Regression; Wind power; Wind speed forecasting