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Title	Research On Key Technologies Of High Energy Efficiency And Low Power Consumption Of New Data Acquisition Equipment Of Power Internet Of Things Based On Artificial Intelligence
ID_Doc	45476
Authors	Li X.; Zhao H.; Feng Y.; Li J.; Zhao Y.; Wang X.
Year	2024
Published	International Journal of Thermofluids, 21
DOI	http://dx.doi.org/10.1016/j.ijft.2024.100575
Abstract	Energy efficiency is a critical problem that drives consideration of smart cities and urban areas' development. Energy security and the smart environment face enormous problems because of the dramatic rise in energy consumption brought on by rising population levels and the widespread use of new data-collecting technologies. Traditional smart grids can be updated with IoT-based smart metering (SM) and advanced metering infrastructure (AMI) technologies by revealing previously hidden information about electrical power by implementing a communication system between utilities and consumers during the power transaction process. The smart distribution and energy consumption in smart city environments are strongly supported by the Internet of Things (IoT) and Artificial Intelligence (AI). Hence, this paper suggests the IoT and AI-assisted Smart Metering System (IoT–AI–SMS) as a new data acquisition equipment for predicting energy consumption in smart cities. The information is taken from Energy Efficiency Datasets to examine smart cities' energy consumption. This research offers a Recurrent Neural Network (RNN) for load forecasting using smart meter data. This technique allows training a single model with all participating smart meters without exchanging local information. Considering the customers' needs, the model developed scheduled the controllable loads and offered optimal dispatch of distributed generation in the smart grid. © 2024
Author Keywords	Artificial intelligence; Energy efficiency; Internet of Things; Load forecasting; Low power consumption; Optimization; Recurrent neural network; Smart grid; Smart meter

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