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.
1. 1 Introduction - p. 1 2. 2 Data science, statistics, and time series - p. 13 3. 3 Machine learning - p. 41 4. 4 Introduction to machine learning in the power generation industry - p. 77 5. 5 Data management from the DCS to the historian and HMI - p. 93 6. 6 Getting the most across the value chain - p. 123 7. 7 Project management for a machine learning project - p. 135 8. 8 Machine learning-based PV power forecasting methods for electrical grid management and energy trading - p. 165 9. 9 Electrical consumption forecasting in hospital facilities - p. 195 10. 10 Soft sensors for NOx emissions - p. 213 11. 11 Variable identification for power plant efficiency - p. 227 12. 12 Forecasting wind power plant failures - p. 241 13. Index - p. 255
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.
1. 1 Introduction - p. 1 2. 2 Data science, statistics, and time series - p. 13 3. 3 Machine learning - p. 41 4. 4 Introduction to machine learning in the power generation industry - p. 77 5. 5 Data management from the DCS to the historian and HMI - p. 93 6. 6 Getting the most across the value chain - p. 123 7. 7 Project management for a machine learning project - p. 135 8. 8 Machine learning-based PV power forecasting methods for electrical grid management and energy trading - p. 165 9. 9 Electrical consumption forecasting in hospital facilities - p. 195 10. 10 Soft sensors for NOx emissions - p. 213 11. 11 Variable identification for power plant efficiency - p. 227 12. 12 Forecasting wind power plant failures - p. 241 13. Index - p. 255
