Journal
ENERGY REPORTS
Volume 9, Issue -, Pages 503-509Publisher
ELSEVIER
DOI: 10.1016/j.egyr.2023.01.071
Keywords
Load monitoring; Internet of things; Big data; Deep learning; Smart home
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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.
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/).
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