Application of machine learning and deep learning methods to power system problems

by Abdar, Moloud Asadi, Somayeh Jebelli, Houtan Mohammadi-Ivatloo, Behnam Nazari-Heris, Morteza Sadat-Mohammadi, Milad

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Title Statements

Full title :Application of machine learning and deep learning methods to power system problems / Morteza Nazari-Heris, Somayeh Asadi, Behnam Mohammadi-Ivatloo, Moloud Abdar, Houtan Jebelli, Milad Sadat-Mohammadi, editors.

Summary

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. Offers innovative machine learning and deep learning methods for dealing with power system issues; Provides promising solution methodologies; Covers theoretical background and experimental analysis.

Edition Statements

Publication Date:2022

Publication Statements

Publisher(s):Springer
Place of Publication:Cham, Switzerland
Language:eng
Publication Date:2022

Notes

Includes index.

The Physical Object

Description:1 online resource (ix, 391 pages) : : illustrations

Edition Identifiers

ISBN:9783030776961, 3030776964
DOI:10.1007/978-3-030-77696-1
oclc:on1280484539
LC Classification:TK1005
Dewey Decimal:621.310285/63

Chapter 1. Power System Challenges and Issues -- Chapter 2. Introduction and literature review of power system challenges and issues -- Chapter 3. Machine learning and power system planning: opportunities, and challenges -- Chapter 4. Introduction to Machine Learning Methods in Energy Engineering -- Chapter 5. Introduction and Literature Review of the Application of Machine Learning/Deep Learning to Control Problems of Power Systems -- Chapter 6. Introduction and literature review of the application of machine learning/deep learning to load forecasting in power system -- Chapter 7. A Survey of Recent particle swarm optimization (PSO)-Based Clustering Approaches to Energy Efficiency in Wireless Sensor Networks -- Chapter 8. Clustering in Power Systems Using Innovative Machine Learning/Deep Learning Methods -- Chapter 9. Voltage stability assessment in power grids using novel machine learning-based methods -- Chapter 10. Evaluation and Classification of cascading failure occurrence potential due to line outage -- Chapter 11. LSTM-Assisted Heating Energy Demand Management in Residential Buildings -- Chapter 12. Wind Speed Forecasting Using Innovative Regression Applications of Machin…