Energy production predication via Internet of Thing based machine learning system
Source of Publication
Future Generation Computer Systems
© 2019 Elsevier B.V. Wind energy is an interesting source of alternative energy to complement the Brazilian energy matrix. However, one of the great challenges lies in managing this resource, due to its uncertainty behavior. This study addresses the estimation of the electric power generation of a wind turbine, so that this energy can be used efficiently and sustainable. Real wind and power data generated in set of wind turbines installed in a wind farm in Ceará State, Brazil, were used to obtain the power curve from a wind turbine using logistic regression, integrated with Nonlinear Autoregressive neural networks to forecast wind speeds. In our system the average error in power generation estimate is of 29 W for 5 days ahead forecast. We decreased the error in the manufacturer's power curve in 63%, with a logics regression approach, providing a 2.7 times more accurate estimate. The results have a large potential impact for the wind farm managers since it could drive not only the operation and maintenance but management level of energy sells.
Modeling, Nonlinear Autoregressive, Power curve, Time series, Wind power
Rebouças Filho, Pedro P.; Gomes, Samuel L.; e Nascimento, Navar M.Mendonça; Medeiros, Cláudio M.S.; Outay, Fatma; and de Albuquerque, Victor Hugo C., "Energy production predication via Internet of Thing based machine learning system" (2019). All Works. 1486.
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