Machine Learning and Data Science in the Power Generation Industry

Best Practices, Tools, and Case Studies
Erscheinungsjahr 2021
ISBN: 978-0-12-819742-4
Verlag: William Andrew Publishing

Buch, Englisch, 274 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 590 g

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.

Zielgruppe

<p>Power industry expert and practitioner working either in engineering, production, maintenance or management. Individual contributor in charge of actually carrying out a project or a manager of all levels who wants to create a project, product, or service based on ML. Graduate students and early career researchers working in power systems and power generation, or in computational aspects of power.</p>

Autoren/Hrsg.

Bangert, Patrick

Fachgebiete

• Technische Wissenschaften Energietechnik | Elektrotechnik Energietechnik & Elektrotechnik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik

Inhaltsverzeichnis

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

Über Autor(innen)

Bangert, Patrick
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.