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Smart city article details

Title	Intelligent Solar Energy Harvesting And Management In Iot Nodes Using Deep Self-Organizing Maps
ID_Doc	32548
Authors	Shinkar A.R.; Joshi D.; Praveen R.V.S.; Rajesh Y.; Boopalan K.; Singh D.
Year	2024
Published	2nd International Conference on Emerging Research in Computational Science, ICERCS 2024
DOI	http://dx.doi.org/10.1109/ICERCS63125.2024.10895729
Abstract	Smart city energy generation must be efficient and dependable. As a result of studies conducted in this field, reliable control schemes for microgrid management have been developed, which seamlessly integrate with smart building management systems. In order for a building microgrid's solar energy system to recover from problems, this article suggests reliable controllers and the hardware to install them. Training models and Internet of Things (IoT) sensors provide the backbone of this proposed approach. When it comes to organizational and industrial contexts, sensors have evolved significantly with the introduction of the IoT. Pressure, optical, temperature, chemical sensors, and proximity are just a few examples of the many types of data that IoT devices may collect and send through sensor networks, allowing for greater efficiency. The model achieves a 91.37% accuracy rate in solar energy harvesting prediction after being trained using the Extended DSOM. © 2024 IEEE.
Author Keywords	IoT Sensors; Maximum Power Point Tracking; Piezoelectric Components; Self-Organizing Map (SOM); Solar Energy Harvesting

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