Adaptive Artificial Intelligence–Driven Optimization Techniques for Energy-Aware Electronic Systems

Abstract

The rapid evolution of intelligent electronic systems has led to remarkable improvements in computational power, connectivity, and automation across diverse application areas; however, these advancements have also resulted in significantly increased energy consumption, giving rise to concerns related to sustainability, thermal management, and operational efficiency. As a result, energy-aware electronic system design has become a key research priority. This study explores adaptive artificial intelligence (AI)–driven optimization techniques aimed at improving energy efficiency while maintaining system performance and reliability. An integrated framework is proposed that leverages machine learning, deep learning, and reinforcement learning to dynamically regulate power consumption in electronic systems. Unlike conventional static or rule-based power management methods, adaptive AI-driven approaches continuously learn from real-time operational data, enabling intelligent decision-making under varying workloads and environmental conditions. The framework monitors critical parameters including power usage, processing load, temperature, and execution latency to identify optimal energy-efficient configurations. A comprehensive methodology encompassing system modelling, data collection, AI model training, adaptive optimization, and performance evaluation is employed. The results indicate that AI-based optimization strategies can reduce energy consumption by approximately 20% to 35% without compromising system throughput or reliability. Reinforcement learning models demonstrate strong adaptability in dynamic environments, while hybrid AI approaches provide effective trade-offs between optimization accuracy and computational overhead. Overall, the findings establish adaptive AI-driven optimization as a practical and scalable solution for next-generation energy-efficient electronic systems, contributing to the advancement of sustainable intelligent computing architectures and low-power electronic design.