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Application of Machine Learning and Deep Learning Methods to Power System Problems de Morteza Nazari-Heris

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

Editat de Morteza Nazari-Heris, Somayeh Asadi, Behnam Mohammadi-Ivatloo, Moloud Abdar, Houtan Jebelli, Milad Sadat-Mohammadi
en Limba Engleză Hardback – 21 oct 2021
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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Specificații

ISBN-13: 9783030776954
ISBN-10: 3030776956
Pagini: 391
Ilustrații: IX, 391 p. 120 illus.
Dimensiuni: 155 x 235 mm
Greutate: 0.77 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seria Power Systems

Locul publicării:Cham, Switzerland

Cuprins

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 dueto 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.

Notă biografică

Morteza Nazari-Heris, PhD,  is a Research Assistant in the Department of Architectural Engineering at The Pennsylvania State University. He obtained BSc and MSc degrees in electrical engineering from the University of Tabriz. His main areas of interest are micro grids, smart grids, integrated heat and power networks, and energy storage technologies. He has received several awards and fellowships, including outstanding national master and PhD student, selection among the top-rated applicants from the College of Engineering in recognition of the strength of academic record, three Outstanding Thesis Awards, and selection as IEEE PES DAY 2021 Ambassador in the IEEE Section Category from Central Pennsylvania Section. He also was selected as the Architectural Engineering Outstanding Graduate Student Awards and also received the Borda Graduate Scholarship in Honor of Gifford H. Albright-Scholarly Excellence. He serves as an editor and reviewer for a number of journals and symposia and is an active member of professional communities like Institute of Electrical and Electronics Engineers (IEEE), Clean Energy Leadership Institute, and Young Professionals in Energy.
Somayeh Asadi, PhD, is an Associate Professor in the Department of Architectural Engineering at the Pennsylvania State University. She is also a Fulbright Specialist. Prior to that, she was an Assistant Professor at the Texas A&M University. She specializes in sustainable building and built environment, integrated building design systems, energy management, renewable energy systems, and smart grids. To date, her research efforts have resulted in more than 80 peer-reviewed journal articles, more than 10 book chapters, two books, and more than 60 peer-reviewed conference papers. Her research efforts include 12 externally funded projects as a PI and Co-PI, including 3 from the National Science Foundation, 3 from the Pennsylvania Department of Environmental Protection, 3 from the US Department of Energy, and 3 from Qatar National Research Foundation, resulting in more than $5 million in research project funding in total. She specializes in sustainable building and built environment, integrated building design systems, energy management, renewable energy systems, and smart grids.

Behnam Mohammadi-Ivatloo, PhD, is a Senior Research Fellow at Aalborg University, Aalborg, Denmark. Prior to that, he was Associate Professor at the University of Tabriz, Tabriz, Iran. Before joining the University of Tabriz, he was a research associate at Institute for Sustainable Energy, Environment and Economy at the University of Calgary. He obtained MSc and PhD degrees in electrical engineering from Sharif University of Technology. Dr. Mohammadi is head of the Smart Energy Systems Lab and his mains research interests are renewable energies, micro grid systems, and smart grids.Moloud Abdar  received the bachelor’s degree in computer engineeringfrom Damghan University, Damghan, Semnan, Iran, in 2015, and the master’s degree in computer science and engineering from the University of Aizu, Aizu, Fukushima, Japan, in 2018. He is currently pursuing the Ph.D. degree with the Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia. He has written several articles in the fields of data mining, machine learning, deep learning and user modelling in some refereed international journals and conferences. He is also very active in several international conferences, including the TPC in the ECML PKDD 2020, IEEE AINA 2018-2020, IEEE NCA 2020 and several referred international journals as a Reviewer. He was a recipient of the Fonds de Recherche du Quebec—Nature et Technologies Award (ranked 5th among 20 candidates in the second round of selection process), in 2019. His research interests include data mining, machine learning, sentiment analysis, deep learning and medical image analysis.
Houtan Jebelli, PhD,  is an Assistant Professor in the Department of Architectural Engineering and Affiliate of the Institute for Computational and Data Sciences at the Pennsylvania State University. He earned his Ph.D. in Civil Engineering from the University of Michigan. He received his bachelor′s degree in Civil Engineering from Tehran Polytechnic University and an MSc in Structural Engineering from the Sharif University of Technology. Dr. Jebelli completed a second MSc in Construction Engineering and Management from the University of Nebraska-Lincoln. While pursuing his Ph.D., he received his third MSc in Electrical Engineering and Computer Science. Dr. Jebelli is Director of the Robotic, Automation, and Intelligent Sensing (RAISE) Lab. Dr. Jebelli's research group at Penn State explores novel approaches that infuse human physiology into robotic control and motion planning system to augment awareness and adaptation between workers and robots. His team's other thematic priorities involve physiological computing for construction automation and safety, physiologically-enabled health monitoring of construction workers, brain-driven approaches for teleoperation, and personalized worker-robot collaboration and co-adaptation at construction sites. He has received several awards and fellowships, including the Charles M. Eastman Top Ph.D. Paper Award, two Outstanding Thesis Awards, the Rackham Research Grant, the John L. Tishman Fellowship, and the Calvin C. Solem Foundation Fellowship. He serves as a reviewer for a number of journals and symposia and is an active member of professional communities like the ASCE Data Sensing and Analysis (DSA) and ASCE Visualization, Information Modelling, and Simulation (VIMS) committees. Dr. Jebelli has over 60 published peer reviewed articles.

Milad Sadat-Mohammadi is a Research Assistant at the Pennsylvania State University. Milad is pursuing his doctoral degree as well as his second master's degree in electrical engineering and computer science focused on deep learning and machine learning. He earned his first master's degree in electrical engineering (focused on planning and managing energy systems) from Tehran Polytechnic University. His research interests include smart energy systems, intelligent energy management in residential and commercial buildings, machine learning, deep learning applications in energy systems, energy storage technologies, and renewable energy sources. Milad has received several awards and scholarships, including the best undergraduate student award, best thesis award, and Borda Graduate Scholarship, respectively, in 2016, 2019, and 2021. He has published several peer-reviewed articles, serves as a reviewer for a number of journals, and is an active member of professional communities.

 


Textul de pe ultima copertă

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.


Caracteristici

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