UTILIZING ARTIFICIAL INTELLIGENCE IN ENERGY MANAGEMENT SYSTEMS TO IMPROVE CARBON EMISSION REDUCTION AND SUSTAINABILITY

Abstract

This article examines the revolutionary potential of artificial intelligence (AI) in improving energy management systems (EMS) to reduce carbon emissions and tackle pressing climate change issues. We conduct a comprehensive literature analysis to analyze AI-driven solutions for optimizing energy usage, minimizing carbon footprints, and promoting sustainability across diverse industries. Conventional EMS methodologies often depend on static and reactive strategies, limiting their efficacy in the face of increasing global energy needs and regulatory requirements. Conversely, AI-driven EMS provides sophisticated data analytics, predictive maintenance, and real-time optimization, markedly enhancing efficiency and emissions control. Our research includes case studies from both industrial and public sectors that illustrate the quantifiable effects of AI-integrated Energy Management Systems in reducing operating expenses, improving renewable energy integration, and fostering better energy practices. Significant hurdles, such as elevated implementation costs, data privacy issues, and regulatory compliance, are examined with prospective legislative frameworks to promote AI use. We underscore the significance of AI in delivering actionable insights, harmonizing energy practices with climate policy, and promoting a sustainable energy future. This study concludes that AI-driven Energy Management Systems are essential for significant emissions reductions and the development of resilient, eco-efficient energy systems, highlighting the necessity for strategic investment and supportive policies to optimize AI technology's societal and environmental advantages in energy management.