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    1. Data och IT
    2. Databaser

    Intelligent Data Mining and Analysis in Power and Energy Systems

    Models and Applications for Smarter Efficient Power Systems

    AvZita A. Vale,Zita A. Vale

    Inbunden, Engelska, 2022

    Del i serien IEEE Press Series on Power and Energy Systems

    1 615 kr

    Beställningsvara. Skickas inom 5-8 vardagar. Fri frakt över 249 kr.

    Beskrivning

    Intelligent Data Mining and Analysis in Power and Energy Systems A hands-on and current review of data mining and analysis and their applications to power and energy systems In Intelligent Data Mining and Analysis in Power and Energy Systems: Models and Applications for Smarter Efficient Power Systems, the editors assemble a team of distinguished engineers to deliver a practical and incisive review of cutting-edge information on data mining and intelligent data analysis models as they relate to power and energy systems. You’ll find accessible descriptions of state-of-the-art advances in intelligent data mining and analysis and see how they drive innovation and evolution in the development of new technologies. The book combines perspectives from authors distributed around the world with expertise gained in academia and industry. It facilitates review work and identification of critical points in the research and offers insightful commentary on likely future developments in the field. It also provides: A thorough introduction to data mining and analysis, including the foundations of data preparation and a review of various analysis models and methodsIn-depth explorations of clustering, classification, and forecastingIntensive discussions of machine learning applications in power and energy systemsPerfect for power and energy systems designers, planners, operators, and consultants, Intelligent Data Mining and Analysis in Power and Energy Systems will also earn a place in the libraries of software developers, researchers, and students with an interest in data mining and analysis problems.

    Produktinformation

    • Utgivningsdatum:2022-12-09
    • Mått:183 x 260 x 31 mm
    • Vikt:1 202 g
    • Format:Inbunden
    • Språk:Engelska
    • Serie:IEEE Press Series on Power and Energy Systems
    • Antal sidor:448
    • Förlag:John Wiley & Sons Inc
    • ISBN:9781119834021

    Utforska kategorier

    • Databaser inom Data och IT

    Mer om författaren

    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.

    Innehållsförteckning

    • 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 Lezama  3. Deep Learning in Intelligent Power and Energy SystemsBruno Mota, Tiago Pinto, Zita Vale, and Carlos Ramos  PART II. Clustering4. Data Mining Techniques applied to Power SystemsSérgio Ramos, João Soares, Zahra Forouzandeh, and Zita Vale  5. Synchrophasor Data Analytics for Anomaly and Event Detection, Classification and LocalizationSajan K. Sadanandan, A. Ahmed, S. Pandey, and Anurag K. Srivastava  6. Clustering Methods for the Profiling of Electricity Consumers Owning Energy Storage SystemCátia Silva, Pedro Faria, Zita Vale, and Juan Manuel Corchado  PART III. Classification7. A Novel Framework for NTL Detection in Electric Distribution SystemsChia-Chi Chu, Nelson Fabian Avila, Gerardo Figueroa, and Wen-Kai Lu  8. Electricity market participation profiles classification for decision support in market negotiationTiago Pinto and Zita Vale  9. Socio-demographic, economic and behavioural analysis of electric vehiclesRúben Barreto, Tiago Pinto, and Zita Vale  PART IV. Forecasting10. A Multivariate Stochastic Spatio-Temporal Wind Power Scenario Forecasting ModelWenlei Bai, Duehee Lee, and Kwang Y. Lee  11. Spatio-Temporal Solar Irradiance and Temperature Data Predictive EstimationChirath Pathiravasam and Ganesh K. Venayagamoorthy  12. Application of decomposition-based hybrid wind power forecasting in isolated power systems with high renewable energy penetrationEvgenii Semshikov, Michael Negnevitsky, James Hamilton, and Xiaolin Wang  PART V. Data analysis13. Harmonic Dynamic Response Study of Overhead Transmission LinesDharmbir Prasad, Rudra Pratap Singh, Md. Irfan Khan, and Sushri Mukherjee  14. 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 Sharma  15. Intelligent Approaches to Support Demand Response in Microgrid PlanningRahmat Khezri, Amin Mahmoudi, and Hirohisa Aki  16. 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 Masebinu  PART VI. Other machine learning applications17. A Parallel Bidirectional Long Short-Term Memory Model for Non-Intrusive Load MonitoringVictor Andrean and Kuo-Lung Lian  18. Reinforcement Learning for Intelligent Building Energy Management System ControlOlivera Kotevska and Philipp Andelfinger  19. Federated Deep Learning Technique for Power and Energy Systems Data AnalysisHamed Moayyed, Arash Moradzadeh, Behnam Mohammadi-Ivatloo, and Reza Ghorbani  20. Data Mining and Machine Learning for Power System Monitoring, Understanding, and Impact EvaluationXinda Ke, Huiying Ren, Qiuhua Huang, Pavel Etingov and Zhangshuan Hou  ConclusionsZita Vale, Tiago Pinto, Michael Negnevitsky, and Ganesh Kumar Venayagamoorthy