Short-Term Forecast of Wind Turbine Production with Machine Learning Methods: Direct and Indirect Approach

2019 ◽  
pp. 301-315
Author(s):  
Mamadou Dione ◽  
Eric Matzner-Løber
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
Short Term ◽  
Learning Methods ◽  
Indirect Approach ◽  
Short Term Forecast ◽  
Machine Learning Methods ◽  
Term Forecast

scholarly journals Use of machine learning methods in prediction of short-term outcome in autism spectrum disorders

2018 ◽  
Vol 29 (3) ◽  
pp. 320-325 ◽  
Author(s):  
Mirac Baris Usta ◽  
Koray Karabekiroglu ◽  
Berkan Sahin ◽  
Muazzez Aydin ◽  
Abdullah Bozkurt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorders ◽  
Autism Spectrum ◽  
Short Term ◽  
Learning Methods ◽  
Term Outcome ◽  
Machine Learning Methods ◽  
Short Term Outcome ◽  
Spectrum Disorders

scholarly journals Prediction of dynamic mooring responses of a floating wind turbine using an artificial neural network

2021 ◽  
Vol 1201 (1) ◽  
pp. 012023
Author(s):  
F A Bjørni ◽  
S Lien ◽  
T Aa Midtgarden ◽  
G Kulia ◽  
A Verma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
Wave Height ◽  
Significant Wave Height ◽  
Learning Methods ◽  
Mooring Lines ◽  
Peak Period ◽  
Significant Wave ◽  
Machine Learning Methods ◽  
Artificial Neural

Abstract Numerical simulations in coupled aero-hydro-servo-elastic codes are known to be a challenge for design and analysis of offshore wind turbine systems because of the large number of design load cases involved in checking the ultimate and fatigue limit states. To alleviate the simulation burden, machine learning methods can be useful. This article investigates the effect of machine learning methods on predicting the mooring line tension of a spar floating wind turbine. The OC3 Hywind wind turbine with a spar-buoy foundation and three mooring lines is selected and simulated with SIMA. A total of 32 sea states with irregular waves are considered. Artificial neural works with different constructions were applied to reproduce the time history of mooring tensions. The best performing network provides a strong average correlation of 71% and consists of two hidden layers with 35 neurons, using the Bayesian regularisation backpropagation algorithm. Sea states applied in the network training are predicted with greater accuracy than sea states used for validation of the network. The correlation coefficient is primarily higher for sea states with lower significant wave height and peak period. One sea state with a significant wave height of 5 meters and a peak period of 9 seconds has an average extreme value deviation for all mooring lines of 0.46%. Results from the study illustrate the potential of incorporating artificial neural networks in the mooring design process.

scholarly journals Machine learning methods for wind turbine condition monitoring: A review

2019 ◽  
Vol 133 ◽  
pp. 620-635 ◽  
Author(s):  
Adrian Stetco ◽  
Fateme Dinmohammadi ◽  
Xingyu Zhao ◽  
Valentin Robu ◽  
David Flynn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Learning Methods ◽  
Machine Learning Methods
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