Machine Learning and Data Science in the Power Generation Industry

Best Practices, Tools, and Case Studies

Patrick Bangert

• Publisher: Elsevier
• Year: 2021
• Print ISBN:
  9780128197424
• eBook ISBN:
  9780128226001
• Topic: Technology & Engineering
• Subtopic: Electrical Engineering & Telecommunications
• Index: Technology & 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. 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

Table of contents

• Machine Learning and Data Science in the Power Generation Industry
• 1 Introduction
• 2 Data science, statistics, and time series
• 3 Machine learning
• 4 Introduction to machine learning in the power generation industry
• 5 Data management from the DCS to the historian and HMI
• 6 Getting the most across the value chain
• 7 Project management for a machine learning project
• 8 Machine learning-based PV power forecasting methods for electrical grid management and energy trading
• 9 Electrical consumption forecasting in hospital facilities
• 10 Soft sensors for NOx emissions
• 11 Variable identification for power plant efficiency
• 12 Forecasting wind power plant failures
• Index