The increasing demand for sustainable energy solutions necessitates
the integration of intelligent systems into low-power device
architectures. Traditional methods for energy management in such
devices often lack the adaptability required to optimize energy
consumption dynamically. This challenge is compounded by the need
to balance computational efficiency with limited power resources,
especially in IoT and edge devices used for energy monitoring and
control. To address these issues, this work explores the application of
Deep Neural Networks (DNNs) in optimizing energy utilization in low-
power devices. The proposed method leverages DNNs for predictive
analytics, enabling real-time decision-making for energy efficiency. A
lightweight DNN architecture is designed to minimize computational
overhead while maintaining high accuracy in tasks such as energy
demand prediction, load balancing, and fault detection. Additionally,
the model incorporates pruning and quantization techniques to
enhance its performance on resource-constrained devices.
Experimental evaluations conducted on a dataset collected from smart
meters demonstrate the efficacy of the proposed approach. Results
indicate a 25% reduction in power consumption and a 30%
improvement in system efficiency compared to existing methods. This
study highlights the potential of deep learning to revolutionize energy
management systems by providing scalable and adaptive solutions for
low-power architectures, ultimately contributing to the development of
smarter and more sustainable energy systems.
M. Sangeetha1, M. Ponkanagavalli2, S. Arun Mozhi Selvi3, M. Ashkar Mohammed4 Dr. N. G. P. Institute of Technology, India1, Holycross Engineering College, India2,3, University of Technology and Applied Sciences, Sultanate of Oman4
Smart Energy Solutions, Low-Power Devices, Deep Neural Networks, Energy Optimization, IoT Applications
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| Published By : ICTACT
Published In :
ICTACT Journal on Microelectronics ( Volume: 10 , Issue: 4 , Pages: 1962 - 1967 )
Date of Publication :
January 2025
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