"In an era marked by concerns about depleting fuel reserves, escalating emissions, and increasing energy expenses, the global focus has pivoted towards sustainable and green energy solutions. In alignment with the United Nations' Sustainable Development Goals, precisely Goal 7, the world is rallying towards ensuring affordable and clean energy for all. Although the renewable energy sector is expanding rapidly, emphasizing sources like solar, wind, and biomass, there's a notable shift from technical hardware breakthroughs to ensuring cost-efficiency, waste reduction, and net economic gains. Interestingly, while sectors are adopting modern engineering techniques such as machine learning (ML), artificial intelligence (AI), nanotechnology, digital twins, and the Internet of Things (IoT), the renewable energy sector still has untapped potential in these areas. For instance, harnessing ML and AI can significantly enhance the predictability of weather-dependent renewable energy sources. Similarly, nanotechnology can revolutionize thermal storage and heat transport"-- Provided by publisher.

Comprehensive insights into integrating modern engineering techniques with machine learning and renewable energy to create a more sustainable world Through an interdisciplinary approach, Machine Learning for Sustainable Energy Solutions provides comprehensive insights into integrating modern engineering techniques such as machine learning (ML), artificial intelligence (AI), nanotechnology, digital twins, and the Internet of Things (IoT) with renewable energy. Each chapter is based on modern research and enhanced by experimental or simulated data. The book offers a thorough review of several energy storage techniques, helping readers fully grasp the larger background in which chemical, thermal, electrical, mechanical, and machine learning technologies may be used to evaluate, categorize, and maximize different storage systems. The book also reviews the confluence of the Internet of Things (IoT) and machine learning for real-time digestive parameter control and monitoring, along with the cooperative importance of mathematical modeling and artificial intelligence in maximizing reactor performance, gas output, and operational stability. Machine Learning for Sustainable Energy Solutions includes information on: Bio-based energy generation from biomass gasification and biohydrogen Usage of hybrid approaches, support vector machines, and neural networks to anticipate and maximize bioenergy production from challenging organic feedstocks Hydrogen-powered dual-fuel engines, covering response surface methodology (RSM) for multi-attribute optimization Scalable, experimentally confirmed ML-based solutions for long-standing problems like sedimentation, pumping losses, and stability of nanofluids The growing and important use of nanotechnology in energy systems, particularly in engine emissions management, energy storage, and heat transfer improvements Machine Learning for Sustainable Energy Solutions is an essential reference for professionals, researchers, educators, and students working in the fields of energy, environmental science, and machine learning. The book also helps decision-makers in various fields by providing them the required knowledge to make informed choices on sustainable practices and policies.