Download Free Application Of Machine Learning And Deep Learning Methods To Power System Problems Book in PDF and EPUB Free Download. You can read online Application Of Machine Learning And Deep Learning Methods To Power System Problems and write the review.

This book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction, classification, clustering, and fault/event detection in power systems, providing effective and promising solutions for many novel challenges faced by power system operators. Written by leading experts, the book will be an ideal resource for researchers and engineers working in the electrical power engineering and power system planning communities, as well as students in advanced graduate-level courses.
This book covers applications of machine learning in artificial intelligence. The specific topics covered include human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and the social sciences, bioinformatics and robotics, etc. It also provides a broad range of techniques that can be successfully applied and adopted in different areas. Accordingly, the book offers an interesting and insightful read for scholars in the areas of computer vision, speech recognition, healthcare, business, marketing, and bioinformatics.
This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Intelligent Data Mining and Analysis in Power and Energy Systems A hands-on and current review of data mining and analysis and their applications to power and energy systems In Intelligent Data Mining and Analysis in Power and Energy Systems: Models and Applications for Smarter Efficient Power Systems, the editors assemble a team of distinguished engineers to deliver a practical and incisive review of cutting-edge information on data mining and intelligent data analysis models as they relate to power and energy systems. You’ll find accessible descriptions of state-of-the-art advances in intelligent data mining and analysis and see how they drive innovation and evolution in the development of new technologies. The book combines perspectives from authors distributed around the world with expertise gained in academia and industry. It facilitates review work and identification of critical points in the research and offers insightful commentary on likely future developments in the field. It also provides: A thorough introduction to data mining and analysis, including the foundations of data preparation and a review of various analysis models and methods In-depth explorations of clustering, classification, and forecasting Intensive discussions of machine learning applications in power and energy systems Perfect for power and energy systems designers, planners, operators, and consultants, Intelligent Data Mining and Analysis in Power and Energy Systems will also earn a place in the libraries of software developers, researchers, and students with an interest in data mining and analysis problems.
Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. - Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms - Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis - Gives direction to future development trends of AI technologies in chemical and process engineering
As the world grapples with the urgent need for sustainable energy solutions, the limitations of traditional approaches to renewable energy forecasting become increasingly evident. The demand for more accurate predictions in net load forecasting, line loss predictions, and the seamless integration of hybrid solar and battery storage systems is more critical than ever. In response to this challenge, advanced Artificial Intelligence (AI) techniques are emerging as a solution, promising to revolutionize the renewable energy landscape. Machine Learning and Computer Vision for Renewable Energy presents a deep exploration of AI modeling, analysis, performance prediction, and control approaches dedicated to overcoming the pressing issues in renewable energy systems. Transitioning from the complexities of energy prediction to the promise of advanced technology, the book sets its sights on the game-changing potential of computer vision (CV) in the realm of renewable energy. Amidst the struggle to enhance sustainability across industries, CV technology emerges as a powerful ally, collecting invaluable data from digital photos and videos. This data proves instrumental in achieving better energy management, predicting factors affecting renewable energy, and optimizing overall sustainability. Readers, including researchers, academicians, and students, will find themselves immersed in a comprehensive understanding of the AI approaches and CV methodologies that hold the key to resolving the challenges faced by renewable energy systems.
Monitoring and Control of Electrical Power Systems using Machine Learning Techniques bridges the gap between advanced machine learning techniques and their application in the control and monitoring of electrical power systems, particularly relevant for heavily distributed energy systems and real-time application. The book reviews key applications of deep learning, spatio-temporal, and advanced signal processing methods for monitoring power quality. This reference introduces guiding principles for the monitoring and control of power quality disturbances arising from integration of power electronic devices and discusses monitoring and control of electrical power systems using benchmark test systems for the creation of bespoke advanced data analytic algorithms. - Covers advanced applications and solutions for monitoring and control of electrical power systems using machine learning techniques for transmission and distribution systems - Provides deep insight into power quality disturbance detection and classification through machine learning, deep learning, and spatio-temporal algorithms - Includes substantial online supplementary components focusing on dataset generation for machine learning training processes and open-source microgrid model simulators on GitHub
Energy Systems Transition: Digitalization, Decarbonization, Decentralization, and Democratization provides a thorough multidisciplinary overview of the operation of modern green energy systems and examines the role of 4D energy transition in global decarbonization mitigation efforts for meeting long-term climate goals. Contributions present practical aspects and approaches with evidence from applications to real-world energy systems, offering in-depth technical discussions, case studies, and examples to help readers understand the methods, current challenges, and future directions. A hands-on reference to energy distribution systems, it is suitable for researchers and industry practitioners from different branches of engineering, energy, data science, economics, and operation research.
Provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks