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Author:   Patrick Bangert (Vice President of Artificial Intelligence at Samsung SDSA, San Jose, CA, United States, and Founder and Board Chair of Algorithmica Technologies GmbH, Bad Nauheim, Germany)
Publisher:   Elsevier Science Publishing Co Inc
Imprint:   Elsevier Science Publishing Co Inc
Weight:   0.590kg
ISBN:  

9780128197424

ISBN 10:   0128197420
Pages:   274
Publication Date:   18 January 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

1. Introduction Patrick Bangert 2. Data science, statistics, and time series Patrick Bangert 3. Machine learning Patrick Bangert 4. Introduction to machine learning in the power generation industry Patrick Bangert 5. Data management from the DCS to the historian and HMI Jim Crompton 6. Getting the most across the value chain Robert Maglalang 7. Project management for a machine learning project Peter Dabrowski 8. Machine learning-based PV power forecasting methods for electrical grid management and energy trading Marco Pierro, David Moser, and Cristina Cornaro 9. Electrical consumption forecasting in hospital facilities A. Bagnasco, F. Fresi, M. Saviozzi, F. Silvestro, and A. Vinci 10. Soft sensors for NOx emissions Patrick Bangert 11. Variable identification for power plant efficiency Stewart Nicholson and Patrick Bangert 12. Forecasting wind power plant failures Daniel Brenner, Dietmar Tilch, and Patrick Bangert

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Author Information

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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