Intelligent Data Mining and Analysis in Power and Energy Systems: Models and Applications for Smarter Efficient Power Systems

ISBN-13: 9781119834021 / Angielski / Twarda / 2022

Vale, Zita A.

Spis treści

About the EditorsNotes on ContributorsPrefacePART I. Data Mining and Analysis Fundamentals1. FoundationsAnsel Y. Rodríguez González, Angel Díaz Pacheco, Ramón Aranda, and Miguel Angel Carmona2. Data mining and analysis in power and energy systems: an introduction to algorithms and applicationsFernando Lezama3. Deep Learning in Intelligent Power and Energy SystemsBruno Mota, Tiago Pinto, Zita Vale, and Carlos RamosPART II. Clustering4. Data Mining Techniques applied to Power SystemsSérgio Ramos, João Soares, Zahra Forouzandeh, and Zita Vale5. Synchrophasor Data Analytics for Anomaly and Event Detection, Classification and LocalizationSajan K. Sadanandan, A. Ahmed, S. Pandey, and Anurag K. Srivastava6. Clustering Methods for the Profiling of Electricity Consumers Owning Energy Storage SystemCátia Silva, Pedro Faria, Zita Vale, and Juan Manuel CorchadoPART III. Classification7. A Novel Framework for NTL Detection in Electric Distribution SystemsChia-Chi Chu, Nelson Fabian Avila, Gerardo Figueroa, and Wen-Kai Lu8. Electricity market participation profiles classification for decision support in market negotiationTiago Pinto and Zita Vale9. Socio-demographic, economic and behavioural analysis of electric vehiclesRúben Barreto, Tiago Pinto, and Zita ValePART IV. Forecasting10. A Multivariate Stochastic Spatio-Temporal Wind Power Scenario Forecasting ModelWenlei Bai, Duehee Lee, and Kwang Y. Lee11. Spatio-Temporal Solar Irradiance and Temperature Data Predictive EstimationChirath Pathiravasam and Ganesh K. Venayagamoorthy12. Application of decomposition-based hybrid wind power forecasting in isolated power systems with high renewable energy penetrationEvgenii Semshikov, Michael Negnevitsky, James Hamilton, and Xiaolin WangPART V. Data analysis13. Harmonic Dynamic Response Study of Overhead Transmission LinesDharmbir Prasad, Rudra Pratap Singh, Md. Irfan Khan, and Sushri Mukherjee14. Evaluation of Shortest Path to Optimize Distribution Network Cost and Power Losses in Hilly Areas: A Case StudySubho Upadhyay, Rajeev Kumar Chauhan, and Mahendra Pal Sharma15. Intelligent Approaches to Support Demand Response in Microgrid PlanningRahmat Khezri, Amin Mahmoudi, and Hirohisa Aki16. Socio-Economic Analysis of Renewable Energy Interventions: Developing Affordable Small-Scale Household Sustainable Technologies in Northern UgandaJens Bo Holm-Nielsen, Achora Proscovia O Mamur, and Samson MasebinuPART VI. Other machine learning applications17. A Parallel Bidirectional Long Short-Term Memory Model for Non-Intrusive Load MonitoringVictor Andrean and Kuo-Lung Lian18. Reinforcement Learning for Intelligent Building Energy Management System ControlOlivera Kotevska and Philipp Andelfinger19. Federated Deep Learning Technique for Power and Energy Systems Data AnalysisHamed Moayyed, Arash Moradzadeh, Behnam Mohammadi-Ivatloo, and Reza Ghorbani20. Data Mining and Machine Learning for Power System Monitoring, Understanding, and Impact EvaluationXinda Ke, Huiying Ren, Qiuhua Huang, Pavel Etingov and Zhangshuan HouConclusionsZita Vale, Tiago Pinto, Michael Negnevitsky, and Ganesh Kumar Venayagamoorthy

O autorze

Zita Vale, PhD, is a Full Professor in the Electrical Engineering Department at the School of Engineering of the Polytechnic of Porto and Director of the GECAD Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development. She is the Chair of the IEEE PES Working Group on Intelligent Data Mining and Analysis.Tiago Pinto, PhD, is an Assistant Professor at the University of Trás-os-Montes e Alto Douro, and a senior researcher at INESC-TEC, Portugal. During the development of this book he was with the GECAD Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development.Michael Negnevitsky, PhD, is the Chair Professor in Power Engineering and Computational Intelligence, and Director of the Centre for Renewable Energy and Power Systems of the University of Tasmania, Australia.Ganesh Kumar Venayagamoorthy, PhD, is the Duke Energy Distinguished Professor of Electrical and Computer Engineering at Clemson University. He is a Fellow of the IEEE, Institution of Engineering and Technology, South African Institute of Electrical Engineers and Asia-Pacific Artificial Intelligence Association.