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

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