Machine Learning and Data Science in the Power Generation Industry

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 energy industry, with a focus on real-world case studies selected from modern practice. The book provides a set of realistic pathways for organizations seeking to develop machine learning methods, with discussion on data selection and curation, as well as organizational implementation in terms of staffing and continuing operationalization. The book articulates a body of case study-driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, emissions credits, and forecasting. Provides best practices on how to design and setup ML projects in power systems, including all non-technological 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 data must be prepared Determines the specific data needs for the collection, processing, and operationalization of data within machine learning algorithms for power systems Includes numerous supporting real-world case studies, providing practical guidance on best practices and potential pitfalls INDICE: 1. Data Science, Statistics and Time-Series 2. Machine Learning 3. Introduction to Machine Learning in Power Generation 4. Data Management from the DCS to the Historian 5. Designing the Business Case 6. Project Management for an ML Project 7. Choosing the Right Methods and Tools (KPI on how to compare them) 8. Integration of ML into Plant Architecture 9. Case Study 10. Case Study 11. Case Study Appendix: Algorithms and Tools in Practice and Further Reading

• ISBN: 978-0-12-819742-4
• Editorial: Elsevier
• Encuadernacion: Rústica
• Páginas: 316
• Fecha Publicación: 01/10/2020
• Nº Volúmenes: 1
• Idioma: Inglés