OPTIMIZING LOW-POWER DEVICE ARCHITECTURES WITH DEEP NEURAL NETWORKS FOR SMART ENERGY SOLUTIONS

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.