AI-Driven Power Optimization in IoT Devices: Enhancing Efficiency through Intelligent Electronic Control Systems

Abstract

With the exponential growth of the Internet of Things (IoT), optimizing power consumption has become a critical challenge in ensuring the sustainability and efficiency of connected systems. Traditional power management techniques often rely on static algorithms and fail to adapt to dynamic workloads and environmental changes. This study explores the application of Artificial Intelligence (AI) in optimizing power utilization across IoT devices through intelligent electronic control systems. By integrating machine learning algorithms, real-time analytics, and adaptive control strategies, AI enables predictive power regulation, extending battery life and improving energy efficiency. Experimental results indicate that AI-based optimization can reduce power consumption by up to 35% compared to traditional models. The findings highlight the transformative potential of AI in enhancing the reliability, performance, and scalability of IoT networks for industrial, healthcare, and smart home applications.