Real-Time Non-Intrusive Electrical Load Classification Over IoT Using Machine Learning

In this era of technological advancement, the flow of an enormous amount of information has become such an inevitable phenomenon that makes a path for the takeover of the internet of things (IoT) based smart grid from the currently available grid system. In a smart grid, demand-side management plays...

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Autores principales:	Md. Tanvir Ahammed, Md. Mehedi Hasan, Md. Shamsul Arefin, Md. Rafiqul Islam, Md. Aminur Rahman, Eklas Hossain, Md. Tanvir Hasan
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Non-intrusive load monitoring real-time load classification IoT framework machine learning variation of supply voltage Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/315e43e312214815ae385b8e549f6bd6
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id	oai:doaj.org-article:315e43e312214815ae385b8e549f6bd6
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spelling	oai:doaj.org-article:315e43e312214815ae385b8e549f6bd62021-11-19T00:06:58ZReal-Time Non-Intrusive Electrical Load Classification Over IoT Using Machine Learning2169-353610.1109/ACCESS.2021.3104263https://doaj.org/article/315e43e312214815ae385b8e549f6bd62021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9511265/https://doaj.org/toc/2169-3536In this era of technological advancement, the flow of an enormous amount of information has become such an inevitable phenomenon that makes a path for the takeover of the internet of things (IoT) based smart grid from the currently available grid system. In a smart grid, demand-side management plays a crucial role in reducing the generation capacity by shifting the user energy consumption from peak period to off-peak period, which requires detailed knowledge of the user consumption at the individual appliance level. Non-intrusive load monitoring (NILM) provides an exceptionally low-cost solution for determining individual appliance levels using a single-point measurement. This paper proposed an IoT-based real-time non-intrusive load classification (RT-NILC) system considering the variability of supply voltage using low-frequency data. Due to the unavailability of smart meters at the household level in Bangladesh, a data-acquisition system (DAS) is developed. The DAS is capable of measuring and storing rms voltage, rms current, active power, and power factor data at a sampling rate of 1 Hz. These data are processed to train different multilabel classification models. The best-performed classification model has been selected and utilized for the implementation of RT-NILC over IoT. The Firebase real-time online database is considered for data storage to flow the data in two-way between end-user and service provider (energy distributor). The GPRS module is used for wireless data transmission as a Wi-Fi network may not be available everywhere. Windows and web applications are developed for data visualization. The proposed system has been validated in real-time, using rms voltage, rms current, and active power measurements at a real house. Even under supply voltage variability, the performance evaluation of the RT-NILC system has shown an average classification accuracy of more than 94%. Good classification accuracy and the overall operation of the IoT-based information exchange systems ensure the proposed system’s applicability for efficient energy management.Md. Tanvir AhammedMd. Mehedi HasanMd. Shamsul ArefinMd. Rafiqul IslamMd. Aminur RahmanEklas HossainMd. Tanvir HasanIEEEarticleNon-intrusive load monitoringreal-time load classificationIoT frameworkmachine learningvariation of supply voltageElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 115053-115067 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Non-intrusive load monitoring real-time load classification IoT framework machine learning variation of supply voltage Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Non-intrusive load monitoring real-time load classification IoT framework machine learning variation of supply voltage Electrical engineering. Electronics. Nuclear engineering TK1-9971 Md. Tanvir Ahammed Md. Mehedi Hasan Md. Shamsul Arefin Md. Rafiqul Islam Md. Aminur Rahman Eklas Hossain Md. Tanvir Hasan Real-Time Non-Intrusive Electrical Load Classification Over IoT Using Machine Learning
description	In this era of technological advancement, the flow of an enormous amount of information has become such an inevitable phenomenon that makes a path for the takeover of the internet of things (IoT) based smart grid from the currently available grid system. In a smart grid, demand-side management plays a crucial role in reducing the generation capacity by shifting the user energy consumption from peak period to off-peak period, which requires detailed knowledge of the user consumption at the individual appliance level. Non-intrusive load monitoring (NILM) provides an exceptionally low-cost solution for determining individual appliance levels using a single-point measurement. This paper proposed an IoT-based real-time non-intrusive load classification (RT-NILC) system considering the variability of supply voltage using low-frequency data. Due to the unavailability of smart meters at the household level in Bangladesh, a data-acquisition system (DAS) is developed. The DAS is capable of measuring and storing rms voltage, rms current, active power, and power factor data at a sampling rate of 1 Hz. These data are processed to train different multilabel classification models. The best-performed classification model has been selected and utilized for the implementation of RT-NILC over IoT. The Firebase real-time online database is considered for data storage to flow the data in two-way between end-user and service provider (energy distributor). The GPRS module is used for wireless data transmission as a Wi-Fi network may not be available everywhere. Windows and web applications are developed for data visualization. The proposed system has been validated in real-time, using rms voltage, rms current, and active power measurements at a real house. Even under supply voltage variability, the performance evaluation of the RT-NILC system has shown an average classification accuracy of more than 94%. Good classification accuracy and the overall operation of the IoT-based information exchange systems ensure the proposed system’s applicability for efficient energy management.
format	article
author	Md. Tanvir Ahammed Md. Mehedi Hasan Md. Shamsul Arefin Md. Rafiqul Islam Md. Aminur Rahman Eklas Hossain Md. Tanvir Hasan
author_facet	Md. Tanvir Ahammed Md. Mehedi Hasan Md. Shamsul Arefin Md. Rafiqul Islam Md. Aminur Rahman Eklas Hossain Md. Tanvir Hasan
author_sort	Md. Tanvir Ahammed
title	Real-Time Non-Intrusive Electrical Load Classification Over IoT Using Machine Learning
title_short	Real-Time Non-Intrusive Electrical Load Classification Over IoT Using Machine Learning
title_full	Real-Time Non-Intrusive Electrical Load Classification Over IoT Using Machine Learning
title_fullStr	Real-Time Non-Intrusive Electrical Load Classification Over IoT Using Machine Learning
title_full_unstemmed	Real-Time Non-Intrusive Electrical Load Classification Over IoT Using Machine Learning
title_sort	real-time non-intrusive electrical load classification over iot using machine learning
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/315e43e312214815ae385b8e549f6bd6
work_keys_str_mv	AT mdtanvirahammed realtimenonintrusiveelectricalloadclassificationoveriotusingmachinelearning AT mdmehedihasan realtimenonintrusiveelectricalloadclassificationoveriotusingmachinelearning AT mdshamsularefin realtimenonintrusiveelectricalloadclassificationoveriotusingmachinelearning AT mdrafiqulislam realtimenonintrusiveelectricalloadclassificationoveriotusingmachinelearning AT mdaminurrahman realtimenonintrusiveelectricalloadclassificationoveriotusingmachinelearning AT eklashossain realtimenonintrusiveelectricalloadclassificationoveriotusingmachinelearning AT mdtanvirhasan realtimenonintrusiveelectricalloadclassificationoveriotusingmachinelearning
_version_	1718420600870928384

Real-Time Non-Intrusive Electrical Load Classification Over IoT Using Machine Learning

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