Machine Learning and Data Science in the Power Generation Industry Best Practices, Tools, and Case Studies
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- Taschenbuch
- eBook ausgewählt
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Form:Einzelkauf Download
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Sprache:Englisch
122,00 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB 3
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
14.01.2021
Herausgeber
Patrick BangertVerlag
Elsevier Science & Techn.Seitenzahl
274 (Printausgabe)
Dateigröße
42134 KB
Sprache
Englisch
EAN
9780128226001
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.
- 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
- 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
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