Optimization metaheuristics for minimizing variance in a real-world statistical application

Authors:
Estevão Costa;Fábio Fabris;Alexandre Rodrigues Loureiros;Hannu Ahonen;Flávio Miguel Varejão
Affiliations:
Universidade Federal do Espírito Santo, Goiabeiras, Vitória, Espírito Santo, Brazil;Universidade Federal do Espírito Santo, Goiabeiras, Vitória, Espírito Santo, Brazil;Universidade Federal do Espírito Santo, Goiabeiras, Vitória, Espírito Santo, Brazil;Universidade Federal do Espírito Santo, Goiabeiras, Vitória, Espírito Santo, Brazil;Universidade Federal do Espírito Santo, Goiabeiras, Vitória, Espírito Santo, Brazil
Venue:
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Year:
2013

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IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2

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Abstract

Non-technical energy losses mostly arise from illegal use of energy. Energy distribution companies need to estimate the sources of these losses in order to take actions for reducing them. In this work we formulate a stratified sampling procedure as a non-linear restricted optimization problem, in which the variance of overall energy loss due to the fraudulent activities is minimized. Solving this problem analytically is difficult and therefore we customize two metaheuristics, Genetic Algorithm and Simulated Annealing, for finding practical solutions for the problem. Numerical experiments and comparison to a proportional allocation scheme are presented.