Application of Machine Learning and Deep Learning Methods to Power System Problems

By Marylou Dantonio and Paul Beisenherz

This book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction, classification, clustering, and fault/event detection in power systems, providing effective and promising solutions for many novel challenges faced by power system operators. Written by leading experts, the book will be an ideal resource for researchers and engineers working in the electrical power engineering and power system planning communities, as well as students in advanced graduate-level courses.

• Format: Paperback
• Language: English
• ISBN: 3030776980
• ISBN13: 9783030776985
• Release Date: October 2022
• Publisher: Springer
• Length: 391 Pages
• Weight: 1.24 lbs.
• Dimensions: 0.8" x 6.1" x 9.2"

Customer Reviews

OOP concepts are clearly explained

I've just purchased a copy of this book and am enjoying reading it. It's written in a very clear and concise manner and a breeze to read. The OOP concepts are clearly explained with accompanying diagrams. Does exactly what it says on the tin.

Good introduction to object oriented programming!

This is a good book for programmers wanting to learn about the basics of object oriented programming and its implementation in VB.Net. Unlike other books that dive into so much technical detail that you end up glossing over the information and not actually learning all that much, this book clearly and simply walks you through the basics of OOPs. After each section, you are given examples of code to see how the concepts...