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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
Power industry experts and practitioners working either in engineering, production, maintenance or management. Individual contributors in charge of actually carrying out a project or managers at all levels who want to create a project, product, or service based on ML. Graduate students and early career researchers working in power systems and power generation, or in computational aspects of power
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
- No. of pages:
- © Elsevier 2021
- 1st November 2020
- Paperback ISBN:
Patrick Bangert is founder and CEO of Algorithmica Technologies, providing real-time process modeling, optimization and predictive maintenance solutions to the process industry with a focus on chemistry and power generation. He is also CTO of APO Offshore Inc, a leading data analytics company responsible for developing predictive maintenance techniques in the O&G industry. His doctorate from UCL specialized in Applied Mathematics, and his academic positions at NASA jet propulsion laboratory and Los Alamos National Laboratory made use of optimization and machine learning for magnetohydrodynamics and particle accelerator experiments. He has published extensively across optimization and machine learning, and relevant applications in the real world.
CEO Algorithmica Technologies, Cupertino, California, USA
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