Machine Learning and Data Science in the Power Generation Industry: Best Practices, Tools, and Case Studies

by Patrick Bangert (Editor)

Overview

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. - Provides best practices on how to design and set up ML projects in power systems, including all nontechnological aspects necessary to be successful - Explores implementation pathways, explaining key ML algorithms and approaches as well as the choices that must be made, how to make them, what outcomes may be expected, and how the data must be prepared for them - Determines the specific data needs for the collection, processing, and operationalization of data within machine learning algorithms for power systems - Accompanied by numerous supporting real-world case studies, providing practical evidence of both best practices and potential pitfalls

Product Details

ISBN-13:	9780128226001
Publisher:	Elsevier Science
Publication date:	01/14/2021
Sold by:	Barnes & Noble
Format:	eBook
Pages:	274
File size:	43 MB
Note:	This product may take a few minutes to download.

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.

Table of Contents

1. IntroductionPatrick Bangert2. Data science, statistics, and time seriesPatrick Bangert3. Machine learningPatrick Bangert4. Introduction to machine learning in the power generation industryPatrick Bangert5. Data management from the DCS to the historian and HMIJim Crompton6. Getting the most across the value chainRobert Maglalang7. Project management for a machine learning projectPeter Dabrowski8. Machine learning-based PV power forecasting methods for electrical grid management and energy tradingMarco Pierro, David Moser, and Cristina Cornaro9. Electrical consumption forecasting in hospital facilitiesA. Bagnasco, F. Fresi, M. Saviozzi, F. Silvestro, and A. Vinci10. Soft sensors for NOx emissionsPatrick Bangert11. Variable identification for power plant efficiencyStewart Nicholson and Patrick Bangert12. Forecasting wind power plant failuresDaniel Brenner, Dietmar Tilch, and Patrick Bangert

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From the Publisher

Explores cutting-edge research in machine learning and applies it to problems in the power-generation industry to highlight current best practices