Artificial Neural Networks for Renewable Energy Systems and Real-World Applications

Artificial Neural Networks for Renewable Energy Systems and Real-World Applications is a comprehensive reference that highlights the application of ANN in renewable energy systems and manufacturing processes. It provides a single resource for researchers and engineers interested in artificial networks, renewable energy, and manufacturing process analysis, covering current trends and providing a broader perspective.

Format: Paperback / softback
Length: 288 pages
Publication date: 21 September 2022
Publisher: Elsevier Science Publishing Co Inc

Artificial Neural Networks for Renewable Energy Systems and Real-World Applications delves into the forefront of addressing intricate engineering challenges within the realm of application, modeling, analysis, and optimization of diverse energy systems and manufacturing processes. As the research landscape expands to encompass the utilization of neural networks in specific industrial applications, this comprehensive reference serves as a valuable resource that offers a broader perspective on Artificial Neural Networks (ANNs) in renewable energy systems and manufacturing processes. The appeal of ANN-based methodologies has garnered the attention of scientists and researchers across various engineering and industrial domains, rendering this book an indispensable resource for all scholars and engineers with an interest in artificial networks, renewable energy systems, and the analysis of manufacturing processes.

The book encompasses a comprehensive exploration of ANN architectures, algorithms, and applications, spanning from renewable energy systems to manufacturing processes. It provides a detailed account of the latest advancements, research trends, and case studies, fostering a deeper understanding of ANN's potential in addressing complex problems and optimizing performance.

Chapter 1 introduces the fundamentals of ANN, including their structure, training methods, and applications. It provides a solid foundation for readers who are new to the field, while also offering insights for those with prior knowledge. Chapter 2 delves into the application of ANN in renewable energy systems, highlighting their role in optimizing power generation, energy storage, and renewable energy integration. It discusses various ANN architectures, such as Feedforward Neural Networks (FNNs), Recurrent Neural Networks (RNNs), and Deep Learning Networks (DLNs), and their suitability for different renewable energy applications. Chapter 3 explores the use of ANN in manufacturing processes, focusing on process modeling, optimization, and quality control. It discusses ANN-based methods for identifying patterns, predicting outcomes, and improving process efficiency. Chapter 4 discusses the challenges and limitations of ANN in renewable energy systems and manufacturing processes. It explores the issues related to data scarcity, model validation, and real-world implementation. Chapter 5 presents future research directions and opportunities in the field of ANN for renewable energy systems and manufacturing processes. It discusses emerging technologies, such as quantum computing and edge computing, and their potential applications in ANN-based solutions.

In conclusion, Artificial Neural Networks for Renewable Energy Systems and Real-World Applications serves as a vital resource for researchers, engineers, and practitioners seeking to leverage the power of ANN in addressing complex engineering problems in renewable energy systems and manufacturing processes. With its comprehensive coverage, up-to-date research, and practical insights, this book empowers readers to stay at the forefront of this rapidly evolving field.

Dimension: 229 x 152 (mm)
ISBN-13: 9780128207932