The pursuit of reducing and rationalizing electricity consumption is increasingly becoming a priority for all consumers worldwide. Residential environments are responsible for a large part of electricity consumption. Non-intrusive load monitoring systems were created with the aim of helping consumers, providing the possibility of obtaining information about the individual consumption of equipment and thus allowing a monitored consumption and the consequent increase in energy efficiency. In a Non-Intrusive Load Monitoring System, four steps are critical: acquiring aggregate data through a single sensor, detecting equipment on / off events from the aggregate load, extracting disaggregated signal characteristics and the identification of equipment based on the characteristics extracted from the disaggregated signal. In this context, this work proposes a new methodology for identification of electrical equipment in a residential environment employing a competitive structure of Auto-Associative Neural Networks. The proposed system is based on power signal measurements obtained from equipment on / off events. To test the proposed methodology 3 scenarios were developed using 3 different public databases. Due to the good results achieved, analyzed using statistical metrics, it is evaluated that the proposed methodology is able to efficiently perform the task of identifying electrical equipment, thus contributing to the development of future non-intrusive monitoring systems. meet market demands.
|
