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

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Application of Machine Learning and Deep Learning Methods to Power System Problems/ edited by Morteza Nazari-Heris, Somayeh Asadi, Behnam Mohammadi-Ivatloo, Moloud Abdar, Houtan Jebelli, Milad Sadat-Mohammadi.
其他作者:	Sadat-Mohammadi, Milad.
面頁冊數:	IX, 391 p. 120 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Energy Policy, Economics and Management. -
電子資源:	https://doi.org/10.1007/978-3-030-77696-1
ISBN:	9783030776961

Application of Machine Learning and Deep Learning Methods to Power System Problems
Application of Machine Learning and Deep Learning Methods to Power System Problems[electronic resource] /edited by Morteza Nazari-Heris, Somayeh Asadi, Behnam Mohammadi-Ivatloo, Moloud Abdar, Houtan Jebelli, Milad Sadat-Mohammadi. - 1st ed. 2021. - IX, 391 p. 120 illus.online resource. - Power Systems,1860-4676. - Power Systems,.

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 Machine Learning Techniques -- Chapter 13. Effective Load Pattern Classification by Processing the Smart Meter Data Based on Event-Driven Processing and Machine Learning -- Chapter 14. Prediction of Out-of-step Condition for Synchronous Generators Using Decision Tree Based on the Dynamic data by WAMS/PMU -- Chapter 15. The adaptive neuro-fuzzy inference system model for short-term load, price and topology forecasting of distribution system -- Chapter 16. Application of Machine Learning for Predicting User Preferences in Optimal Scheduling of Smart Appliances -- Chapter 17. Machine Learning Approaches in a Real Power System and Power Markets.

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.

ISBN: 9783030776961

Standard No.: 10.1007/978-3-030-77696-1doiSubjects--Topical Terms:

784769
Energy Policy, Economics and Management.

LC Class. No.: TK7881.15

Dewey Class. No.: 621.317

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