AI-Driven Smart Energy Management for Sustainable Development in Organizations

Abstract

This paper presents a comprehensive study of AI-driven smart energy management systems (SEMS) and their role in fostering sustainable development within organizations. As global energy demands continue to rise, organizations face mounting pressure to reduce carbon footprints while maintaining operational efficiency. This work examines how machine learning, deep learning, and reinforcement learning techniques can be integrated into organizational energy infrastructures to enable real-time demand forecasting, adaptive load control, and renewable energy optimization. We analyze real-world deployments across industrial, commercial, and institutional sectors, and propose a unified AI-SEMS framework. Results indicate that AI-enabled systems can achieve 15–35% reductions in organizational energy consumption while meaningfully advancing UN Sustainable Development Goals 7, 9, and 13. Challenges including data privacy, interoperability, and equitable deployment are also discussed.