Machine Learning-Integrated Sustainable Engineering and Energy Systems

Abstract

Machine learning (ML) has revolutionized various fields, including engineering, energy systems, and sustainability. This abstract explores the synergies between ML and these domains, focusing on its optimization in predictive maintenance, energy consumption efficiency, and smart grids. ML's role in renewable energy forecasting, building energy management, and materials science is also explored. It also highlights its impact on supply chain optimization, environmental monitoring, and sustainability assessments. The holistic approach extends to smart city initiatives and infrastructure development, paving the way for intelligent urban planning. ML enhances decision-making processes, enabling more resilient, efficient, and sustainable practices in engineering and energy systems. This exploration serves as a beacon for researchers, practitioners, and policymakers seeking innovative solutions at the intersection of ML, engineering, and sustainability.

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