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,

• author = "Aneela Zameer and Junaid Arshad and Asifullah Khan and Muhammad Asif Zahoor Raja",
• title = "Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks",
• journal = "Energy Conversion and Management",
• volume = "134",
• pages = "361--372",
• year = "2017",
• ISSN = "0196-8904",
• DOI = "doi:10.1016/j.enconman.2016.12.032",
• URL = "http://www.sciencedirect.com/science/article/pii/S0196890416311189",
• 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.",
• 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

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