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.
| ISBN: | 9780128197424 |
| Publication date: | 18th January 2021 |
| Author: | Patrick Bangert |
| Publisher: | Elsevier an imprint of Elsevier Science |
| Format: | Paperback |
| Pagination: | 316 pages |
| Genres: |
Energy, power generation, distribution and storage Electrical engineering |
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.
Machine Learning and Data Science in the Power Generation Industry features in the following genres: Energy, power generation, distribution and storage, Electrical engineering
Paperback. Not Available.
Machine Learning and Data Science in the Power Generation Industry was written by Patrick Bangert and published by Elsevier an imprint of Elsevier Science
Machine Learning and Data Science in the Power Generation Industry has 316 pages