Scenario Reduction via Clustering in R^n

• Grant number: 24/07004-6
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: August 01, 2024
• End date: July 31, 2025
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Luis Augusto Angelotti Meira
• Grantee: Danilo Barcellos Corrêa
• Host Institution: Faculdade de Tecnologia (FT). Universidade Estadual de Campinas (UNICAMP). Limeira , SP, Brazil
• Company: Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Mecânica (FEM)
• Associated research grant: 17/15736-3 - Engineering Research Centre in Reservoir and Production Management, AP.PCPE

Abstract

The student will to work on the problem of selecting representative models, which can also be referred to as scenario reduction. Creating scenarios is a common activity in situations with uncertainty. When the number of scenarios is large, ranging from hundreds to thousands, it is necessary to perform a scenario reduction while maintaining representativeness. The technique currently used by researchers at CEPETRO combines objectives, involving risk curves, crossplots, and two functions operating on categorical attributes. The goal of this research is to test the hypothesis that replacing the crossplot with clustering in R^n will bring benefits to scenario reduction.