Energy Efficient and Sustainable Computing Models for Intelligent Autonomous Systems A Comparative Analysis
Year: 2026
Type: article
Abstract: Energy-Efficient and Sustainable Computing Models for Intelligent Autonomous Systems Research Description This research paper presents a comparative analysis of energy-efficient and sustainable computing models for intelligent autonomous systems. The study investigates the relationship between: Autonomy Levels AI Model Architectures Training Time Energy Consumption CO₂ Emissions Research Methodology Historical benchmark datasets and simulation-based prediction techniques were used to estimate future sustainability challenges associated with AI systems. "Higher autonomy levels significantly increase computational complexity, energy consumption, and environmental impact." Key Findings High-autonomy systems require longer training time. Energy usage increases with deeper architectures. Lightweight AI models improve sustainability. Green AI practices reduce environmental impact. Technologies & Concepts Artificial Intelligence Machine Learning Green Computing Sustainable AI Autonomous Systems Conclusion The findings support the development of environmentally responsible and energy-efficient intelligent systems aligned with long-term sustainability goals. Read Full Research Paper Authors: Aayan Abdul Mannan Shaikh, Rishi Poddar, Ms. Jyotsna Anthal
Authors: shaikh, Aayan, Poddar, Rishi, Anthal, Jyotsna
Language: English
Topic: Green IT and Sustainability
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7161180439
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