Electricity Demand Forecasting: The Uruguayan Case

  • Andrés Castrillejo
  • Jairo CugliariEmail author
  • Fernando Massa
  • Ignacio Ramirez
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 254)


The development of new electricity generation technologies has given new opportunities to developing economies. These economies are often highly dependent on fossil sources and so on the price of petrol. Uruguay has finished the transformation of its energetic mix, presenting today a very large participation of renewable sources among its production mix. This rapid change has demanded new mathematical and computing methods for the administration and monitoring of the system load. In this work we present enercast, a R package that contains prediction models that can be used by the network operator. The prediction models are divided in two groups, exogenous and endogenous models, that respectively uses external covariates or not. Each model is used to produce daily prediction which are then combined using a sequential aggregation algorithm. We show by numerical experiments the appropriateness of our end-to-end procedure applied to real data from the Uruguayan electrical system.


Electricity demand forecast Time series Sequential aggregation 



This work was partially funded by Agencia Nacional de Investigación e Innovación (ANII, Uruguay), grant FSE 2013/1/10886, “Modelos de previsión de demanda de corto plazo.”


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Andrés Castrillejo
    • 1
  • Jairo Cugliari
    • 2
    Email author
  • Fernando Massa
    • 1
  • Ignacio Ramirez
    • 3
  1. 1.IESTA, Fac. CCEEUniversidad de la RepublicaMontevideoUruguay
  2. 2.Université de LyonLyonFrance
  3. 3.IEE, Fac. de IngenieríaUniversidad de la RepublicaMontevideoUruguay

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