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.
TopIntroduction
Machine Learning (ML) is transforming sustainable engineering and energy systems by reshaping traditional paradigms and fostering a more sustainable future. Its ability to discern intricate patterns within vast datasets and adaptive learning mechanisms makes it a crucial enabler in addressing complex issues within these sectors. As the global community strives to reduce environmental impact, optimize resource usage, and enhance efficiency, ML is a key enabler in achieving these goals. The convergence of ML with sustainable engineering and energy systems represents a pivotal moment in our pursuit of innovative solutions(Rangel-Martinez et al., 2021).
This section delves into the fundamental principles and applications of machine learning (ML) in engineering, highlighting its role in enhancing decision-making processes, optimizing workflows, and predicting maintenance needs. It also discusses the potential of ML in laying the groundwork for sustainable practices by identifying patterns and making predictions(Zhou et al., 2020). The increasing global energy demand necessitates a shift towards sustainable energy systems, focusing on renewable energy sources, smart grids, and efficient consumption. Machine learning (ML) is crucial in this context, providing predictive capabilities for renewable energy outputs, optimizing energy distribution through smart grids, and enabling real-time adjustments to fluctuating energy demands, thus transforming the energy landscape towards sustainability(Willard et al., 2022).
Machine learning (ML) is a key tool in engineering, particularly in predictive maintenance. By analyzing historical data and real-time sensor inputs, ML algorithms can predict equipment failures, reducing downtime and environmental impact. This chapter explores the integration of ML in sustainable engineering and energy systems, focusing on optimizing energy consumption in buildings, forecasting renewable energy outputs, and revolutionizing materials science(Duchesne et al., 2020). The goal is to demonstrate ML's transformative potential in driving engineering practices towards sustainability. In the ever-evolving landscape of engineering and energy systems, the integration of Machine Learning (ML) stands as a transformative force, ushering in a new era of efficiency, resilience, and sustainability. This introduction sets the stage for a comprehensive exploration into the profound impacts of ML applications in sustainable engineering practices and energy systems(Yang et al., 2020).
In recent years, Machine Learning has emerged as a paradigm-shifting technology with the potential to revolutionize traditional engineering practices and reshape the landscape of energy systems. At its core, ML is a branch of artificial intelligence that empowers systems to learn and adapt from data, enabling them to make informed decisions without explicit programming. The integration of ML in engineering applications and energy systems has opened up unprecedented possibilities for optimization, predictive analysis, and data-driven decision-making(Ibrahim et al., 2020). In engineering, the traditional approach to system maintenance often involved reactive responses to equipment failures, leading to downtime and substantial costs. However, with ML algorithms, a paradigm shift has occurred towards predictive maintenance. These algorithms analyze vast datasets from sensors and historical records, predicting potential equipment failures before they occur. This proactive strategy not only minimizes downtime but also maximizes the lifespan of machinery, translating to significant cost savings and increased operational efficiency(Ibrahim et al., 2020).