Extreme learning approach with wavelet transform function for forecasting wind turbine wake effect to improve wind farm efficiency
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- @Article{Mladenovic:2016:AES,
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author = "Igor Mladenovic and Dusan Markovic and
Milos Milovancevic and Miroljub Nikolic",
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title = "Extreme learning approach with wavelet transform
function for forecasting wind turbine wake effect to
improve wind farm efficiency",
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journal = "Advances in Engineering Software",
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volume = "96",
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pages = "91--95",
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year = "2016",
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ISSN = "0965-9978",
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DOI = "doi:10.1016/j.advengsoft.2016.02.011",
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URL = "http://www.sciencedirect.com/science/article/pii/S0965997816300588",
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abstract = "A wind turbine operating in the wake of another
turbine and has a reduced power production because of a
lower wind speed after rotor. The flow field in the
wake behind the first row turbines is characterized by
a significant deficit in wind velocity and increased
levels of turbulence intensity. To maximize the wind
farm net profit, the number of turbines installed in
the wind farm should be different in depend on wind
farm project investment parameters. Therefore modelling
wake effect is necessary because it has a great
influence on the actual energy output of a wind farm.
In this paper, the extreme learning machine (ELM)
coupled with wavelet transform (ELM-WAVELET) is used
for the prediction of wind turbine wake effect in wind
far. Estimation and prediction results of ELM-WAVELET
model are compared with the ELM, genetic programming
(GP), support vector machine (SVM) and artificial
neural network (ANN) models. The following error and
correlation functions are applied to evaluate the
proposed models: Root Mean Square Error (RMSE),
Coefficient of Determination (R2) and Pearson
coefficient (r). The experimental results show that an
improvement in predictive accuracy and capability of
generalization can be achieved by ELM-WAVELET approach
(RMSE = 0.269) in comparison with the ELM (RMSE =
0.27), SVM (RMSE = 0.432), ANN (RMSE = 0.432) and GP
model (RMSE = 0.433).",
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keywords = "genetic algorithms, genetic programming, Wind turbine,
Wake model, Wind speed, Soft computing, Forecasting",
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notes = "University of Nis, Faculty of Economics, Trg kralja
Aleksandra 11, Serbia",
- }
Genetic Programming entries for
Igor Mladenovic
Dusan Markovic
Milos Milovancevic
Miroljub Nikolic
Citations