| Full text | |
| Author(s): |
Total Authors: 4
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| Affiliation: | [1] Sao Paulo State Univ UNESP, Dept Energy Engn, Ave Barrageiros 1881, Rosana, SP - Brazil
[2] Univ Porto, Fac Engn, Rua Dr Roberto Frias S-N, P-4200465 Porto - Portugal
[3] INESC TEC, Power Syst Unit, Rua Dr Roberto Frias S-N, P-4200465 Porto - Portugal
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | IEEE Latin America Transactions; v. 18, n. 11, p. 2011-2018, NOV 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
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. (AU) | |
| FAPESP's process: | 18/05341-4 - Electric load forecasting and machine learning: models, analysis and applications |
| Grantee: | Matheus Pussaignolli de Paula |
| Support Opportunities: | Scholarships in Brazil - Scientific Initiation |