Effects of environmental and turbine parameters on energy gains from wind farm system: Artificial neural network simulations. Issue 2 (April 2020)

• Record Type: Journal Article
• Authors: Abidoye, Luqman K; Bani-Hani, Ehab; El Haj Assad, Mamdouh; AlShabi, Mohammad; Soudan, Bassel; Oriaje, Aremu T
• Is Part Of: Wind engineering. Volume 44:Issue 2(2020)
• Page Start: 181
• Page End: 195
• Publication Date: 2020-04
• Subjects: Artificial neural network -- wind farm -- wind turbine -- energy gains -- rotor diameter; Wind-pressure -- Periodicals; Winds -- Periodicals; Wind power -- Periodicals; Engineering meteorology -- Periodicals; Pression du vent; Vents; Énergie éolienne; Météorologie appliquée; Engineering meteorology; Wind power; Wind-pressure; Winds; Periodicals; 621.4505
• DOI: 10.1177/0309524X19849834
• Languages: English
• ISSNs: 0309-524X

Abstract

Artificial neural network modelling has been employed to investigate the effects of various environmental and machine factors on the energy gain from wind farm systems. Numerical comparison of artificial neural network and nonlinear regression from XLSTAT showed that ANN possessed better numerical accuracy in predicting multivariate data. Several artificial neural network models are developed and tested with several structures to obtain the best prediction performance in energy gain from different wind farms in Jordan. The best performing artificial neural network model was used to predict the energy gain from wind farm based on changes in annual wind speed, turbine rotor diameter and turbine power. As a result of 20% increase in turbine power, 14.4%–31% energy gains were recorded across different wind farms. The proposed artificial neural network model was also a good predictor for energy cost resulting from specific wind farm design.