Energy production predication via Internet of Thing based machine learning system

• Published in: Future generation computer systems Vol. 97; pp. 180 - 193
• Main Authors: Rebouças Filho, Pedro P., Gomes, Samuel L., e Nascimento, Navar M. Mendonça, Medeiros, Cláudio M.S., Outay, Fatma, de Albuquerque, Victor Hugo C.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2019
• Subjects: Modeling; Nonlinear Autoregressive; Power curve; Time series; Wind power; Nonlinear Autoregressive; Wind power; Power curve; Modeling; Time series
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2019.01.020

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. •Estimation of the electric power production of a wind turbine.•IoT-based machine learning to predict energy production.•Real wind and power data generated in aerogenerators installed in a wind farm in Ceará State, Brazil.•To obtain the power curve using logistic regression, integrated with Recursive Neural Network to forecast wind speeds.