Machine Learning and Data Science in the Power Generation Industry

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.

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