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{ "item_title" : "Application of Machine Learning and Deep Learning Methods to Power System Problems", "item_author" : [" Morteza Nazari-Heris", "Somayeh Asadi", "Behnam Mohammadi-Ivatloo "], "item_description" : "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.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/3/03/077/698/3030776980_b.jpg", "price_data" : { "retail_price" : "169.99", "online_price" : "169.99", "our_price" : "169.99", "club_price" : "169.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Application of Machine Learning and Deep Learning Methods to Power System Problems|Morteza Nazari-Heris

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

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Overview

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.

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Details

  • ISBN-13: 9783030776985
  • ISBN-10: 3030776980
  • Publisher: Springer
  • Publish Date: October 2022
  • Dimensions: 9.21 x 6.14 x 0.83 inches
  • Shipping Weight: 1.24 pounds
  • Page Count: 391

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