Efficient Computation of SOM for Outage Database
Department of Computer Science, FEECS, VSB – Technical University of Ostrava, 17. Listopadu 15/2172, 708 33 Ostrava-Poruba, Czech Republic
Conference Elnet. Ostrava: VSB-TU Ostrava, 51-63, 2011
@article{gajdos2011efficient,
title={Efficient Computation of SOM for Outage Database},
author={Gajdo{v{s}}, P. and Kr{‘a}tk{‘y}, M. and Bedn{‘a}r, D. and Baca, R. and Gono, R. and Walder, J.},
journal={ELNET 2011},
pages={51}year={2011}
}
This paper describes a utilization of the Self Organizing Map (SOM) method for the analysis of power outage data. SOM, to be already used in many fields, is based on the Kohonen self-organizing neural network and it is known to capture underlying concepts. We apply this method for a unified database of power outages to be collected for several years in the Czech Republic. The most significant attributes are selected from the database and records are used for the training of the SOM. We utilize our previously introduced application EDAS (Electrical Data Analysis using SOM) for the visualization, understanding, and analysis of the trained SOM. Because of performance issues in the previous introduced approaches, we implement our SOM on GPU environment and compare this method with previous solutions in this article.
February 5, 2012 by hgpu
