Patrick Bangert
Elsevier Science
e druk, 2021
9780128197424
Machine Learning and Data Science in the Power Generation Industry
Best Practices, Tools, and Case Studies
Specificaties
Paperback, blz.
|
Engels
Elsevier Science |
e druk, 2021
ISBN13: 9780128197424
Rubricering
Verwachte levertijd ongeveer 8 werkdagen
Samenvatting
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.
Specificaties
Inhoudsopgave
1. Introduction<br>Patrick Bangert<br>2. Data science, statistics, and time series<br>Patrick Bangert<br>3. Machine learning<br>Patrick Bangert<br>4. Introduction to machine learning in the power generation industry<br>Patrick Bangert<br>5. Data management from the DCS to the historian and HMI<br>Jim Crompton<br>6. Getting the most across the value chain<br>Robert Maglalang<br>7. Project management for a machine learning project<br>Peter Dabrowski<br>8. Machine learning-based PV power forecasting methods for electrical grid management and energy trading<br>Marco Pierro, David Moser, and Cristina Cornaro<br>9. Electrical consumption forecasting in hospital facilities<br>A. Bagnasco, F. Fresi, M. Saviozzi, F. Silvestro, and A. Vinci<br>10. Soft sensors for NOx emissions<br>Patrick Bangert<br>11. Variable identification for power plant efficiency<br>Stewart Nicholson and Patrick Bangert<br>12. Forecasting wind power plant failures<br>Daniel Brenner, Dietmar Tilch, and Patrick Bangert