An IoT deep learning-based home appliances management and classification system

The rise in household energy consumption globally has increased the necessity for effective electricity consumption management and load monitoring. Smart meters can facilitate fine-grained analysis by providing consumption insights even at the level of individual appliances, for detecting deterioration of appliances, anomalous behavior, and demand response. In this work, we propose a smart home appliance classification that utilizes the deep learning architecture of Long Short-Term Memory (LSTM) trained on the latest version of the Plug-Load Appliance Identification Database (PLAID). The model achieves competitive precision, recall and F1-scores across 16 different home appliances manufactured by 330 vendors. The model is then deployed on a Raspberry Pi micro-controller and interfaced with smart meters in a home to generate almost real-time classification of appliances and transmit this to a cloud database. The results and insights are made accessible to the end user or utility provider through a mobile application connected to the same database. (c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under theCCBYlicense (http://creativecommons.org/licenses/by/4.0/).

Automated summary

The increase in household energy consumption globally has highlighted the need for effective management and monitoring of electricity usage. This study proposes a smart home appliance classification system that utilizes deep learning and a comprehensive database for training, achieving competitive results across various appliances. The model is deployed on a Raspberry Pi micro-controller and interfaces with smart meters to provide almost real-time appliance classification to end users or utility providers through a mobile application.