Название: Machine Learning and Data Science in the Power Generation Industry: Best Practices, Tools, and Case StudiesАвтор: Patrick Bangert
Издательство: Elsevier
Год: 2021
Страниц: 262
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
Master expert techniques for building automated and highly scalable end-to-end Machine Learning models and pipelines in Azure using TensorFlow, Spark, and Kubernetes. This book will present an overview of data science and Machine Learning (ML) and point toward the many use cases in the power industry where these have already solved problems. The hype surrounding Machine Learning will hopefully be cleared up as this book focusses on what can realistically be done with existing tools.
Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add in developing data-oriented computational programs in the power industry, with a particular focus on thoughtfully chosen real-world case studies. It provides a set of realistic pathways for organizations seeking to develop Machine Learning methods, with a discussion on data selection and curation as well as organizational implementation in terms of staffing and continuing operationalization. It articulates a body of case study driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, and forecasting.
About the Author:
Dr. Patrick Bangert is the Vice President of Artificial Intelligence at Samsung SDS where he leads both the AI software development and AI consulting groups that each provide various offerings to the industry. He is the founder and Board Chair of Algorithmica Technologies, providing real-time process modeling, optimization, and predictive maintenance solutions to the process industry with a focus on chemistry and power generation. His doctorate from UCL specialized in applied mathematics, and his academic positions at NASA’s Jet Propulsion Laboratory and Los Alamos National Laboratory made use of optimization and machine learning for magnetohydrodynamics and particle accelerator experiments. He has published extensively across optimization and machine learning and their relevant applications in the real world.
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