Semiparametric prediction models for variables related with energy production

Abstract

Abstract In this paper a review of semiparametric models developed throughout the years thanks to an 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 sulphur 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 a simulation study and with the prediction of the levels of two important pollution indicators in the environment of the power station: sulphur dioxide and nitrogen oxides.

Keywords

• Semiparametric prediction models
• Pollution indicators
• Cointegration