Authors
Diego P. Pinto-Roa1,3*, Hernán Medina3, Federico Román4, Miguel García-Torres1,2, 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.