Fast Electrical Load Classification using a Dimmer-based Smart Plug

Nemeth, Dániel István and Tornai, Kálmán (2023) Fast Electrical Load Classification using a Dimmer-based Smart Plug. In: Smart Cities, Green Technologies, and Intelligent Transport Systems. Springer, Cham, pp. 68-89. ISBN 978-3-031-37470-8

Text CCIS_cikk.pdf - Accepted Version Restricted to Registered users only Download (3MB)

Abstract

As renewable energy resources become a significant source of electricity production, the stable operation of the electrical grid becomes increasingly difficult. Demand-side control of the electrical grid load solves this problem and enables better utilization of renewable energy such as wind or solar power. Adjusting the grid load to meet the renewable production levels requires knowledge about the composition of the grid load as well as the ability to schedule individual loads. We propose a Smart Plug solution capable of accurately classifying the connected electrical load as well as running the Neural Network-based classification on the Smart Plug. The Smart Plug is WiFi-capable allowing wireless measurements as well as remote control of the connected electrical load. We took measurements with the Smart Plug prototype of common household electrical loads and achieved very high accuracy. This accuracy rate can be achieved with on-device measurement and on-device NN inference in less than 2.5 s. Multiple NN-based classification methods and measurements of different amounts of data were examined (measurement profiles).

Actions (login required)

Edit Item