Neural Discriminating Analysis for a Nonintrusive Electrical Load Monitoring System - Seixas, J. M.; Calôba, L. P.; Prado, C. B.; Aguiar, J. C. R. (Anais do International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), 1997)
A nonintrusive electrical load monitoring system for household appliances is developed using neural networks. Appliances are characterized by features extracted from their transient and steady-state responses obtained from sampling information from the AC power line. A discriminating analysis is applied as an efficient way to achieve a compact neural discriminator which identifies seven classes of equipment. Over 100 different pieces of equipment studied, the system classifies correctly more than 90% of the sample. The system is implemented on a 16 node transputer based parallel machine to support massive application. A processing time smaller than 2µs is achieved for each pattern.