Wind Power Prediction Using Genetic Programming Based Ensemble of Artificial Neural Networks (GPeANN)
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Arshad:2014:FIT,
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author = "Junaid Arshad and Aneela Zameer and Asifullah Khan",
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booktitle = "12th International Conference on Frontiers of
Information Technology (FIT)",
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title = "Wind Power Prediction Using Genetic Programming Based
Ensemble of Artificial Neural Networks ({GPeANN})",
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year = "2014",
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pages = "257--262",
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abstract = "Over the past couple of years, the share of wind power
in electrical power system has increased considerably.
Because of the irregular characteristics of wind, the
power generated by the wind turbines fluctuates
continuously. The unstable nature of the wind power
thus poses a serious challenge in power distribution
systems. For reliable power distribution, wind power
prediction system has become an essential component in
power distribution systems. In this Paper, a wind power
forecasting strategy composed of Artificial Neural
Networks (ANN) and Genetic Programming (GP) is
proposed. Five neural networks each having different
structure and different learning algorithm were used as
base regressors. Then the prediction of these neural
networks along with the original data is used as input
for GP based ensemble predictor. The proposed wind
power forecasting strategy is applied to the data from
five wind farms located in same region of Europe.
Numerical results and comparison with existing wind
power forecasting strategies demonstrates the
efficiency of the proposed strategy.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/FIT.2014.55",
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month = dec,
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notes = "Also known as \cite{7118409}",
- }
Genetic Programming entries for
Junaid Arshad
Aneela Zameer
Asifullah Khan
Citations