Intelligent Decision Support Systems for Energy-Efficient Autonomous Systems in Consumer Electronics

Abstract

This paper proposes an AI-based decision support system for enhancing energy efficiency in autonomous systems within consumer electronics. The primary objective is to optimize energy consumption by leveraging advanced Recurrent Neural Networks (RNNs) combined with Adaptive Moment Estimation (Adam). The proposed intelligent framework incorporates real-time data processing, prediction, and adaptive decision-making to reduce energy usage across a variety of devices, such as smart thermostats, robotic vacuum cleaners, and IoT-enabled lighting systems. Key methodologies include the integration of RNNs to model temporal energy consumption patterns and the Adam optimizer for efficient model training. The results demonstrate significant energy savings with improvements in prediction accuracy compared to traditional approaches. The findings suggest that the AI-based system can provide substantial reductions in energy consumption while maintaining or improving device performance. The research has important implications for the future of energy-efficient consumer electronics, particularly as the demand for smart devices grows.