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Author Guevara, E.; Babonneau, F.; Homem-de-Mello, T.; Moret, S. doi  openurl
  Title A machine learning and distributionally robust optimization framework for strategic energy planning under uncertainty Type Journal Article
  Year 2020 Publication Applied Energy Abbreviated Journal Appl. Energy  
  Volume 271 Issue Pages 18 pp  
  Keywords Strategic energy planning; Electricity generation; Uncertainty; Distributionally robust optimization; Machine learning  
  Abstract This paper investigates how the choice of stochastic approaches and distribution assumptions impacts strategic investment decisions in energy planning problems. We formulate a two-stage stochastic programming model assuming different distributions for the input parameters and show that there is significant discrepancy among the associated stochastic solutions and other robust solutions published in the literature. To remedy this sensitivity issue, we propose a combined machine learning and distributionally robust optimization (DRO) approach which produces more robust and stable strategic investment decisions with respect to uncertainty assumptions. DRO is applied to deal with ambiguous probability distributions and Machine Learning is used to restrict the DRO model to a subset of important uncertain parameters ensuring computational tractability. Finally, we perform an out-of-sample simulation process to evaluate solutions performances. The Swiss energy system is used as a case study all along the paper to validate the approach.  
  Address [Guevara, Esnil] Univ Adolfo Ibanez, PhD Program Ind Engn & Operat Res, Santiago, Chile, Email: frederic.babonneau@uai.cl  
  Corporate Author Thesis  
  Publisher Elsevier Sci Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0306-2619 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000540436500003 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 1188  
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