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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 lowfrequency data. Due to the unavailability of smart meters at the household level in Bangladesh, a dataacquisition 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 application 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 IoTbased information exchange systems ensure the proposed system's applicability for efficient energy management.INDEX TERMS Non-intrusive load monitoring, Real-time load classification, IoT framework, Machine learning, Variation of supply voltage.

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Selection of proper switching frequency of an inverter is very dominant factor for system reliability and performance. Model predictive control (MPC) strategy selects optimal switching state in every sampling instant for the inverter by diminishing cost function. As a result, MPC based multilevel inverter requires higher sampling frequency in comparison with pulse width modulation (PWM) which incurs higher switching frequency. So, optimal selection of switching frequency is required for enhancing system reliability and performance. This paper presents an optimum switching frequency technique for three-level neutral-point clamped (3L-NPC) inverter. The cost function of MPC based 3L-NPC inverter has three control objectives namely current tracking error, neutral point voltage balancing and, the number of switching transitions are included in the cost function with weighting factor. The value of the weighting factor in the cost function is selected by making a trade-off among the current total harmonic distortion (THD), neutral voltage balancing and, the average switching frequency. To evaluate the system performance switching loss is determined at the optimal switching frequency as well as without the switching frequency optimization.