Machine Learning and Data Science in the Power Generation Industry -

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Résumé :
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.

Sommaire:
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