Volume 11, Number 11, July 2021

Parallel Evolutionary Biclustering of Short-term Electric Energy Consumption

  Authors

Diego P. Pinto-Roa^1,3*, Hernán Medina³, Federico Román⁴, Miguel García-Torres^1,2, Federico Divina¹, Francisco Gómez-Vela¹, Félix Morales¹, Gustavo Velázquez¹, Federico Daumas¹, José L. VázquezNoguera¹, Carlos Sauer Ayala³ and Pedro E. Gardel-Sotomayor⁴, ¹Universidad Americana, Paraguay, ²Universidad Pablo de la Ovide, Spain, ³Universidad Nacional de Asunción, Paraguay, ⁴Universidad Católica de Asunción, Paraguay

  Abstract

The discovery and description of patterns in electric energy consumption time series is fundamental for timely management of the system. A bicluster describes a subset of observation points in a time period in which a consumption pattern occurs as abrupt changes or instabilities homogeneously. Nevertheless, the pattern detection complexity increases with the number of observation points and samples of the study period. In this context, current bi-clustering techniques may not detect significant patterns given the increased search space. This study develops a parallel evolutionary computation scheme to find biclusters in electric energy. Numerical simulations show the benefits of the proposed approach, discovering significantly more electricity consumption patterns compared to a state-of-the-art non-parallel competitive algorithm.

  Keywords

Biclustering, Big data, Electric energy consumption, Parallel evolutionary computation.