Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks
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- @Article{Zameer:2017:ECM,
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author = "Aneela Zameer and Junaid Arshad and Asifullah Khan and
Muhammad Asif Zahoor Raja",
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title = "Intelligent and robust prediction of short term wind
power using genetic programming based ensemble of
neural networks",
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journal = "Energy Conversion and Management",
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volume = "134",
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pages = "361--372",
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year = "2017",
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ISSN = "0196-8904",
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DOI = "doi:10.1016/j.enconman.2016.12.032",
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URL = "http://www.sciencedirect.com/science/article/pii/S0196890416311189",
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abstract = "The inherent instability of wind power production
leads to critical problems for smooth power generation
from wind turbines, which then requires an accurate
forecast of wind power. In this study, an effective
short term wind power prediction methodology is
presented, which uses an intelligent ensemble regressor
that comprises Artificial Neural Networks and Genetic
Programming. In contrast to existing series based
combination of wind power predictors, whereby the error
or variation in the leading predictor is propagated
down the stream to the next predictors, the proposed
intelligent ensemble predictor avoids this shortcoming
by introducing Genetical Programming based
semi-stochastic combination of neural networks. It is
observed that the decision of the individual base
regressors may vary due to the frequent and inherent
fluctuations in the atmospheric conditions and thus
meteorological properties. The novelty of the reported
work lies in creating ensemble to generate an
intelligent, collective and robust decision space and
thereby avoiding large errors due to the sensitivity of
the individual wind predictors. The proposed ensemble
based regressor, Genetic Programming based ensemble of
Artificial Neural Networks, has been implemented and
tested on data taken from five different wind farms
located in Europe. Obtained numerical results of the
proposed model in terms of various error measures are
compared with the recent artificial intelligence based
strategies to demonstrate the efficacy of the proposed
scheme. Average root mean squared error of the proposed
model for five wind farms is 0.117575.",
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keywords = "genetic algorithms, genetic programming, Wind power
forecasting, Meterological variables, Regression,
Artificial neural network, Ensemble",
- }
Genetic Programming entries for
Aneela Zameer
Junaid Arshad
Asifullah Khan
Muhammad Asif Zahoor Raja
Citations