{"product_id":"intelligent-data-mining-and-analysis-in-power-and-energy-systems-isbn-9781119834021","title":"Intelligent Data Mining and Analysis in Power and Energy Systems","description":"\u003cb\u003eIntelligent Data Mining and Analysis in Power and Energy Systems\u003c\/b\u003e \u003cp\u003e\u003cb\u003eA hands-on and current review of data mining and analysis and their applications to power and energy systems\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eIn \u003ci\u003eIntelligent Data Mining and Analysis in Power and Energy Systems: Models and Applications for Smarter Efficient Power Systems\u003c\/i\u003e, 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. \u003c\/p\u003e\u003cp\u003eThe 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: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e A thorough introduction to data mining and analysis, including the foundations of data preparation and a review of various analysis models and methods\u003c\/li\u003e \u003cli\u003e In-depth explorations of clustering, classification, and forecasting\u003c\/li\u003e \u003cli\u003e Intensive discussions of machine learning applications in power and energy systems\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for power and energy systems designers, planners, operators, and consultants, \u003ci\u003eIntelligent Data Mining and Analysis in Power and Energy Systems\u003c\/i\u003e will also earn a place in the libraries of software developers, researchers, and students with an interest in data mining and analysis problems. \u003c\/p\u003e\u003cp\u003eAbout the Editors\u003c\/p\u003e \u003cp\u003eNotes on Contributors\u003c\/p\u003e \u003cp\u003ePreface\u003c\/p\u003e \u003cp\u003ePART I. Data Mining and Analysis Fundamentals\u003c\/p\u003e \u003cp\u003e1. Foundations\u003c\/p\u003e \u003cp\u003eAnsel Y. Rodríguez González, Angel Díaz Pacheco, Ramón Aranda, and Miguel Angel Carmona\u003c\/p\u003e \u003cp\u003e2. Data mining and analysis in power and energy systems: an introduction to algorithms and applications\u003c\/p\u003e \u003cp\u003eFernando Lezama\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e3. Deep Learning in Intelligent Power and Energy Systems\u003c\/p\u003e \u003cp\u003eBruno Mota, Tiago Pinto, Zita Vale, and Carlos Ramos\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003ePART II. Clustering\u003c\/p\u003e \u003cp\u003e4. Data Mining Techniques applied to Power Systems\u003c\/p\u003e \u003cp\u003eSérgio Ramos, João Soares, Zahra Forouzandeh, and Zita Vale\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e5. Synchrophasor Data Analytics for Anomaly and Event Detection, Classification and Localization\u003c\/p\u003e \u003cp\u003eSajan K. Sadanandan, A. Ahmed, S. Pandey, and Anurag K. Srivastava\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e6. Clustering Methods for the Profiling of Electricity Consumers Owning Energy Storage System\u003c\/p\u003e \u003cp\u003eCátia Silva, Pedro Faria, Zita Vale, and Juan Manuel Corchado\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003ePART III. Classification\u003c\/p\u003e \u003cp\u003e7. A Novel Framework for NTL Detection in Electric Distribution Systems\u003c\/p\u003e \u003cp\u003eChia-Chi Chu, Nelson Fabian Avila, Gerardo Figueroa, and Wen-Kai Lu\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e8. Electricity market participation profiles classification for decision support in market negotiation\u003c\/p\u003e \u003cp\u003eTiago Pinto and Zita Vale\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e9. Socio-demographic, economic and behavioural analysis of electric vehicles\u003c\/p\u003e \u003cp\u003eRúben Barreto, Tiago Pinto, and Zita Vale\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003ePART IV. Forecasting\u003c\/p\u003e \u003cp\u003e10. A Multivariate Stochastic Spatio-Temporal Wind Power Scenario Forecasting Model\u003c\/p\u003e \u003cp\u003eWenlei Bai, Duehee Lee, and Kwang Y. Lee\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e11. Spatio-Temporal Solar Irradiance and Temperature Data Predictive Estimation\u003c\/p\u003e \u003cp\u003eChirath Pathiravasam and Ganesh K. Venayagamoorthy\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e12. Application of decomposition-based hybrid wind power forecasting in isolated power systems with high renewable energy penetration\u003c\/p\u003e \u003cp\u003eEvgenii Semshikov, Michael Negnevitsky, James Hamilton, and Xiaolin Wang\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003ePART V. Data analysis\u003c\/p\u003e \u003cp\u003e13. Harmonic Dynamic Response Study of Overhead Transmission Lines\u003c\/p\u003e \u003cp\u003eDharmbir Prasad, Rudra Pratap Singh, Md. Irfan Khan, and Sushri Mukherjee\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e14. Evaluation of Shortest Path to Optimize Distribution Network Cost and Power Losses in Hilly Areas: A Case Study\u003c\/p\u003e \u003cp\u003eSubho Upadhyay, Rajeev Kumar Chauhan, and Mahendra Pal Sharma\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e15. Intelligent Approaches to Support Demand Response in Microgrid Planning\u003c\/p\u003e \u003cp\u003eRahmat Khezri, Amin Mahmoudi, and Hirohisa Aki\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e16. Socio-Economic Analysis of Renewable Energy Interventions: Developing Affordable Small-Scale Household Sustainable Technologies in Northern Uganda\u003c\/p\u003e \u003cp\u003eJens Bo Holm-Nielsen, Achora Proscovia O Mamur, and Samson Masebinu\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003ePART VI. Other machine learning applications\u003c\/p\u003e \u003cp\u003e17. A Parallel Bidirectional Long Short-Term Memory Model for Non-Intrusive Load Monitoring\u003c\/p\u003e \u003cp\u003eVictor Andrean and Kuo-Lung Lian\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e18. Reinforcement Learning for Intelligent Building Energy Management System Control\u003c\/p\u003e \u003cp\u003eOlivera Kotevska and Philipp Andelfinger\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e19. Federated Deep Learning Technique for Power and Energy Systems Data Analysis\u003c\/p\u003e \u003cp\u003eHamed Moayyed, Arash Moradzadeh, Behnam Mohammadi-Ivatloo, and Reza Ghorbani\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e20. Data Mining and Machine Learning for Power System Monitoring, Understanding, and Impact Evaluation\u003c\/p\u003e \u003cp\u003eXinda Ke, Huiying Ren, Qiuhua Huang, Pavel Etingov and Zhangshuan Hou\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eConclusions\u003c\/p\u003e \u003cp\u003eZita Vale, Tiago Pinto, Michael Negnevitsky, and Ganesh Kumar Venayagamoorthy\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eZita Vale, PhD,\u003c\/b\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eTiago Pinto, PhD,\u003c\/b\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eMichael Negnevitsky, PhD,\u003c\/b\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eGanesh Kumar Venayagamoorthy, PhD,\u003c\/b\u003e 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.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eA hands-on and current review of data mining and analysis and their applications to power and energy systems\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eIn \u003ci\u003eIntelligent Data Mining and Analysis in Power and Energy Systems: Models and Applications for Smarter Efficient Power Systems\u003c\/i\u003e, 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. \u003c\/p\u003e\u003cp\u003eThe 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: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e A thorough introduction to data mining and analysis, including the foundations of data preparation and a review of various analysis models and methods\u003c\/li\u003e \u003cli\u003e In-depth explorations of clustering, classification, and forecasting\u003c\/li\u003e \u003cli\u003e Intensive discussions of machine learning applications in power and energy systems\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for power and energy systems designers, planners, operators, and consultants, \u003ci\u003eIntelligent Data Mining and Analysis in Power and Energy Systems\u003c\/i\u003e will also earn a place in the libraries of software developers, researchers, and students with an interest in data mining and analysis problems.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47989440544997,"sku":"NP9781119834021","price":150.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119834021.jpg?v=1761784106","url":"https:\/\/k12savings.com\/es\/products\/intelligent-data-mining-and-analysis-in-power-and-energy-systems-isbn-9781119834021","provider":"K12savings","version":"1.0","type":"link"}