Open AccessDomestic smart metering infrastructure and a method for home appliances identification using low‐rate power consumption data
Author(s) -
Paraskevas Ioannis,
Barbarosou Maria,
Fitton Richard,
Swan William
Publication year - 2021
Publication title -
iet smart cities
Language(s) - English
Resource type - Journals
ISSN - 2631-7680
DOI - 10.1049/smc2.12009
Subject(s) - metering mode , smart meter , software deployment , identification (biology) , energy consumption , domain (mathematical analysis) , computer science , consumption (sociology) , smart grid , home automation , power consumption , frequency domain , real time computing , telecommunications , power (physics) , engineering , electrical engineering , mathematics , mechanical engineering , mathematical analysis , social science , botany , physics , quantum mechanics , sociology , biology , computer vision , operating system
The deployment of domestic smart metering infrastructure in Great Britain provides the opportunity for identification of home appliances utilising non‐intrusive load monitoring methods. Identifying the energy consumption of certain home appliances generates useful insights for the energy suppliers and for other bodies with a vested interest in energy consumption. Consequently, the domestic smart metering system, which is an integral part of the smart cities' infrastructure, can also be used for home appliance identification purposes taking into account the limitations of the system. In this article, a step‐by‐step description on accessing data directly from the domestic Smart Meter via an external Consumer Access Device is described, as well as an easy‐to‐implement method for identifying commonly used home appliances through their power consumption signals sampled at a rate similar to the rate available by the domestic smart metering system. The experimental results indicate that the combination of time domain with frequency domain features extracted either from the 1D/2D Discrete Fourier Transform or the Discrete Cosine Transform provides improved recognition performance compared to the case where the time domain or the frequency domain features are used separately.