Abstract
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.
Highlights
Nowadays, people are more involved in ground-breaking technological research in pertinent areas, especially smartThe associate editor coordinating the review of this manuscript and approving it for publication was Tyson Brooks .cities, smart homes, the internet of things (IoT), and others
Cities, smart homes, the internet of things (IoT), and others. These technological advancements are quite demandable in present times - from the original constituents of a city, smart homes, and factories in the fourth industrial revolution smart cities, which are developed by combining IoT with artificial intelligence (AI)
The developed data acquisition system (DAS) is installed at a house, where rms voltage, rms current, active power, and power factor data are collected by turning ON only one appliance at a time for 1-minute
Summary
People are more involved in ground-breaking technological research in pertinent areas, especially smart. To the best of our knowledge, there is no report on real-time load monitoring feasibility over IoT considering supply voltage variability in NILM algorithms. This work demonstrates a practically implementable software and hardware package for real-time non-intrusive load classification (RT-NILC) over IoT using machine learning. The proposed system has been developed by designing a data acquisition system (DAS) that can measure and store rms voltage, rms current, active power, and power factor data to a micro-SD card These data are employed to train and select a machine learning model for the implementation of RT-NILC. 4. Implementation of a real-time IoT-based NILM system using low-frequency data with a complete hardware and software solution.
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