Abstract
In this paper a review of semiparametric models developed throughout the years thanks to extensive collaboration between the Department of Statistics and Operations Research of the University of Santiago de Compostela and a power station located in As Pontes (A Coruña, Spain) property of Endesa Generation, SA, is shown. In particular these models were used to predict the levels of sulfur dioxide in the environment of this power station with half an hour in advance. In this paper also a new multidimensional semiparametric model is considered. This model is a generalization of the previous models and takes into account the correlation structure of errors. Its behaviour is illustrated in the prediction of the levels of two important pollution indicators in the environment of the power station: sulfur dioxide and nitrogen oxides.
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Acknowledgements
The work by Wenceslao González-Manteiga and Manuel Febrero-Bande was partially supported by grants MTM2013-41383-P from Ministerio de Economía y Competitividad, Spain.
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González-Manteiga, W., Febrero-Bande, M., Piñeiro-Lamas, M. (2017). Semiparametric Prediction Models for Variables Related with Energy Production. In: Quintela, P., et al. Progress in Industrial Mathematics at ECMI 2016. ECMI 2016. Mathematics in Industry(), vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-63082-3_1
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