BOOKS - Applications of Deep Machine Learning in Future Energy Systems
Applications of Deep Machine Learning in Future Energy Systems - Mohammad-Hassan Khooban 2024 PDF | EPUB Elsevier BOOKS
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Applications of Deep Machine Learning in Future Energy Systems
Author: Mohammad-Hassan Khooban
Year: 2024
Format: PDF | EPUB
File size: 35.8 MB
Language: ENG



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The book "Applications of Deep Machine Learning in Future Energy Systems" explores the potential of deep machine learning techniques to revolutionize the field of energy systems. The author, a renowned expert in the field, provides a comprehensive overview of the current state of energy systems and the challenges they face, including climate change, energy security, and sustainability. He argues that deep machine learning can provide a powerful tool for addressing these challenges by enabling more efficient, reliable, and adaptable energy systems. The book begins with an introduction to the fundamentals of deep machine learning and its applications in energy systems, providing readers with a solid foundation for understanding the concepts and techniques presented throughout the book. The author then delves into the various applications of deep machine learning in energy systems, including predictive modeling, optimization, and control. Each chapter focuses on a specific application, providing readers with a detailed understanding of how deep machine learning can be used to improve energy efficiency, reduce waste, and increase the resilience of energy systems. One of the key themes of the book is the need to study and understand the process of technology evolution.
Книга «Применение глубокого машинного обучения в будущих энергетических системах» исследует потенциал методов глубокого машинного обучения для революции в области энергетических систем. Автор, известный эксперт в этой области, дает исчерпывающий обзор текущего состояния энергетических систем и проблем, с которыми они сталкиваются, включая изменение климата, энергетическую безопасность и устойчивость. Он утверждает, что глубокое машинное обучение может предоставить мощный инструмент для решения этих проблем, обеспечивая более эффективные, надежные и адаптируемые энергетические системы. Книга начинается с введения в основы глубокого машинного обучения и его применения в энергетических системах, предоставляя читателям прочную основу для понимания концепций и методов, представленных на протяжении всей книги. Затем автор углубляется в различные приложения глубокого машинного обучения в энергетических системах, включая прогнозное моделирование, оптимизацию и управление. Каждая глава посвящена конкретному приложению, предоставляя читателям подробное понимание того, как глубокое машинное обучение может быть использовано для повышения энергоэффективности, сокращения отходов и повышения устойчивости энергетических систем. Одна из ключевых тем книги - необходимость изучения и понимания процесса эволюции технологий.
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