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  • Produktbild: Intelligent Data Mining and Analysis in Power and Energy Systems
  • Produktbild: Intelligent Data Mining and Analysis in Power and Energy Systems

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

167,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

05.12.2022

Herausgeber

Zita A. Vale + weitere

Verlag

John Wiley & Sons

Seitenzahl

448

Maße (L/B/H)

26/18,3/3,1 cm

Gewicht

1202 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-83402-1

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

05.12.2022

Herausgeber

Verlag

John Wiley & Sons

Seitenzahl

448

Maße (L/B/H)

26/18,3/3,1 cm

Gewicht

1202 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-83402-1

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: GPSR Kontakt

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  • Produktbild: Intelligent Data Mining and Analysis in Power and Energy Systems
  • Produktbild: Intelligent Data Mining and Analysis in Power and Energy Systems
  • About the Editors
     
    Notes on Contributors
     
    Preface
     
    PART I. Data Mining and Analysis Fundamentals
     
    1. Foundations
     
    Ansel Y. Rodríguez González, Angel Díaz Pacheco, Ramón Aranda, and Miguel Angel Carmona
     
    2. Data mining and analysis in power and energy systems: an introduction to algorithms and applications
     
    Fernando Lezama
     

    3. Deep Learning in Intelligent Power and Energy Systems
     
    Bruno Mota, Tiago Pinto, Zita Vale, and Carlos Ramos
     

    PART II. Clustering
     
    4. Data Mining Techniques applied to Power Systems
     
    Sérgio Ramos, João Soares, Zahra Forouzandeh, and Zita Vale
     

    5. Synchrophasor Data Analytics for Anomaly and Event Detection, Classification and Localization
     
    Sajan K. Sadanandan, A. Ahmed, S. Pandey, and Anurag K. Srivastava
     

    6. Clustering Methods for the Profiling of Electricity Consumers Owning Energy Storage System
     
    Cátia Silva, Pedro Faria, Zita Vale, and Juan Manuel Corchado
     

    PART III. Classification
     
    7. A Novel Framework for NTL Detection in Electric Distribution Systems
     
    Chia-Chi Chu, Nelson Fabian Avila, Gerardo Figueroa, and Wen-Kai Lu
     

    8. Electricity market participation profiles classification for decision support in market negotiation
     
    Tiago Pinto and Zita Vale
     

    9. Socio-demographic, economic and behavioural analysis of electric vehicles
     
    Rúben Barreto, Tiago Pinto, and Zita Vale
     

    PART IV. Forecasting
     
    10. A Multivariate Stochastic Spatio-Temporal Wind Power Scenario Forecasting Model
     
    Wenlei Bai, Duehee Lee, and Kwang Y. Lee
     

    11. Spatio-Temporal Solar Irradiance and Temperature Data Predictive Estimation
     
    Chirath Pathiravasam and Ganesh K. Venayagamoorthy
     

    12. Application of decomposition-based hybrid wind power forecasting in isolated power systems with high renewable energy penetration
     
    Evgenii Semshikov, Michael Negnevitsky, James Hamilton, and Xiaolin Wang
     

    PART V. Data analysis
     
    13. Harmonic Dynamic Response Study of Overhead Transmission Lines
     
    Dharmbir 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 Study
     
    Subho Upadhyay, Rajeev Kumar Chauhan, and Mahendra Pal Sharma
     

    15. Intelligent Approaches to Support Demand Response in Microgrid Planning
     
    Rahmat Khezri, Amin Mahmoudi, and Hirohisa Aki
     

    16. Socio-Economic Analysis of Renewable Energy Interventions: Developing Affordable Small-Scale Household Sustainable Technologies in Northern Uganda
     
    Jens Bo Holm-Nielsen, Achora Proscovia O Mamur, and Samson Masebinu
     

    PART VI. Other machine learning applications
     
    17. A Parallel Bidirectional Long Short-Term Memory Model for Non-Intrusive Load Monitoring
     
    Victor Andrean and Kuo-Lung Lian
     

    18. Reinforcement Learning for Intelligent Building Energy Management System Control
     
    Olivera Kotevska and Philipp Andelfinger
     

    19. Federated Deep Learning Technique for Power and Energy Systems Data Analysis
     
    Hamed Moayyed, Arash Moradzadeh, Behnam Mohammadi-Ivatloo, and Reza Ghorbani
     

    20. Data Mining and Machine Learning for Power System Monitoring, Understanding, and Impact Evaluation
     
    Xinda Ke, Huiying Ren, Qiuhua Huang, Pavel Etingov and Zhangshuan Hou
     

    Conclusions
     
    Zita Vale, Tiago Pinto, Michael Negnevitsky, and Ganesh Kumar Venayagamoorthy