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Dr. Shailandra Kumar Prasad is an Associate Professor in the Department of Mechanical Engineering at Arka Jain University, Jharkhand, India. His research interests include Renewable Energy Engineering, Sustainable Manufacturing, Mechanical Systems, and Predictive Analytics.
Wind energy has emerged as one of the most promising renewable energy sources for achieving sustainable power generation and reducing dependence on fossil fuels. However, the intermittent and stochastic nature of wind presents significant challenges in power generation planning, grid integration, and energy management. Accurate wind energy forecasting is essential for improving power system reliability, optimizing energy production, and reducing operational uncertainties. Machine Learning (ML) techniques have demonstrated remarkable capabilities in modeling complex nonlinear relationships within meteorological and wind data, enabling highly accurate short-term, medium-term, and long-term forecasting. By leveraging algorithms such as Random Forest, Support Vector Machines, Artificial Neural Networks, Gradient Boosting, and Deep Learning models, wind energy forecasting systems can significantly enhance prediction accuracy and decision-making processes. This chapter explores the principles, methodologies, applications, challenges, and future directions of machine learning-based wind energy forecasting systems, highlighting their contribution to intelligent and sustainable energy management.
The increasing need for clean energy, environmental sustainability, and carbon emission reduction has expedited the implementation of advanced technology in energy management systems. Sustainable energy management necessitates the optimal use of renewable resources, optimised energy use, predictive maintenance, and astute decision-making across several industries. Deep Learning (DL), a potent subset of Artificial Intelligence (AI), has arisen as a disruptive technology adept at analysing intricate energy statistics, predicting demand and generation trends, optimising resource distribution, and improving the efficacy of sustainable technologies. Integrating deep learning with smart grids, renewable energy systems, the Internet of Things (IoT), cloud computing, and digital twin technologies enables organisations to enhance energy efficiency, mitigate environmental impact, and advance sustainable development objectives. This chapter examines the principles, methodology, applications, problems, and future trajectories of deep learning in sustainable energy management and green technology.
The manufacturing sector is experiencing a transformative change due to the advent of Artificial Intelligence (AI), Industry 5.0, and advanced supply chain collaboration. Industry 4.0 prioritised automation and digitalisation, whereas Industry 5.0 underscores human-centric manufacturing, sustainability, resilience, and collaboration between humans and intelligent technology. Artificial intelligence technologies, encompassing machine learning, deep learning, computer vision, robotics, digital twins, and predictive analytics, are revolutionising industrial processes, quality management, predictive maintenance, logistics, and supply chain coordination. AI-driven manufacturing solutions augment operational efficiency, diminish production costs, elevate product quality, and facilitate rapid decision-making inside linked industrial ecosystems. This chapter examines the principles, technologies, applications, difficulties, and future trajectories of AI in manufacturing, Industry 5.0, and collaborative supply chain management, emphasising its contribution to the development of intelligent, sustainable, and resilient industrial organisations.