Wind Power Forecasting in a Semi-Arid Region Based on Machine Learning Error Correction

Wind Power Forecasting in a Semi-Arid Region Based on Machine Learning Error Correction

Wind power forecasting is pivotal in promoting a stable and sustainable grid operation by estimating future power outputs from past meteorological and turbine data. The inherent unpredictability in wind patterns poses substantial challenges in synchronizing supply with demand, with inaccuracies pote...

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Bibliographic Details
Main Authors: Mirella Lima Saraiva Araujo, Yasmin Kaore Lago Kitagawa, Arthur Lúcide Cotta Weyll, Francisco José Lopes de Lima, Thalyta Soares dos Santos, William Duarte Jacondino, Allan Rodrigues Silva, Márcio de Carvalho Filho, Willian Ramires Pires Bezerra, José Bione de Melo Filho, Alex Álisson Bandeira Santos, Diogo Nunes da Silva Ramos, Davidson Martins Moreira
Format: Article
Language:English
Published: MDPI AG 2023-10-01
Series:Wind
Subjects:
machine learning
wind power prediction
short-term forecasting
SCADA
Online Access:https://www.mdpi.com/2674-032X/3/4/28

Internet

https://www.mdpi.com/2674-032X/3/4/28

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