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<p>About the Editors</p> <p>Notes on Contributors</p> <p>Preface</p> <p>PART I. Data Mining and Analysis Fundamentals</p> <p>1. Foundations</p> <p>Ansel Y. Rodríguez González, Angel Díaz Pacheco, Ramón Aranda, and Miguel Angel Carmona</p> <p>2. Data mining and analysis in power and energy systems: an introduction to algorithms and applications</p> <p>Fernando Lezama</p> <p> </p> <p>3. Deep Learning in Intelligent Power and Energy Systems</p> <p>Bruno Mota, Tiago Pinto, Zita Vale, and Carlos Ramos</p> <p> </p> <p>PART II. Clustering</p> <p>4. Data Mining Techniques applied to Power Systems</p> <p>Sérgio Ramos, João Soares, Zahra Forouzandeh, and Zita Vale</p> <p> </p> <p>5. Synchrophasor Data Analytics for Anomaly and Event Detection, Classification and Localization</p> <p>Sajan K. Sadanandan, A. Ahmed, S. Pandey, and Anurag K. Srivastava</p> <p> </p> <p>6. Clustering Methods for the Profiling of Electricity Consumers Owning Energy Storage System</p> <p>Cátia Silva, Pedro Faria, Zita Vale, and Juan Manuel Corchado</p> <p> </p> <p>PART III. Classification</p> <p>7. A Novel Framework for NTL Detection in Electric Distribution Systems</p> <p>Chia-Chi Chu, Nelson Fabian Avila, Gerardo Figueroa, and Wen-Kai Lu</p> <p> </p> <p>8. Electricity market participation profiles classification for decision support in market negotiation</p> <p>Tiago Pinto and Zita Vale</p> <p> </p> <p>9. Socio-demographic, economic and behavioural analysis of electric vehicles</p> <p>Rúben Barreto, Tiago Pinto, and Zita Vale</p> <p> </p> <p>PART IV. Forecasting</p> <p>10. A Multivariate Stochastic Spatio-Temporal Wind Power Scenario Forecasting Model</p> <p>Wenlei Bai, Duehee Lee, and Kwang Y. Lee</p> <p> </p> <p>11. Spatio-Temporal Solar Irradiance and Temperature Data Predictive Estimation</p> <p>Chirath Pathiravasam and Ganesh K. Venayagamoorthy</p> <p> </p> <p>12. Application of decomposition-based hybrid wind power forecasting in isolated power systems with high renewable energy penetration</p> <p>Evgenii Semshikov, Michael Negnevitsky, James Hamilton, and Xiaolin Wang</p> <p> </p> <p>PART V. Data analysis</p> <p>13. Harmonic Dynamic Response Study of Overhead Transmission Lines</p> <p>Dharmbir Prasad, Rudra Pratap Singh, Md. Irfan Khan, and Sushri Mukherjee</p> <p> </p> <p>14. Evaluation of Shortest Path to Optimize Distribution Network Cost and Power Losses in Hilly Areas: A Case Study</p> <p>Subho Upadhyay, Rajeev Kumar Chauhan, and Mahendra Pal Sharma</p> <p> </p> <p>15. Intelligent Approaches to Support Demand Response in Microgrid Planning</p> <p>Rahmat Khezri, Amin Mahmoudi, and Hirohisa Aki</p> <p> </p> <p>16. Socio-Economic Analysis of Renewable Energy Interventions: Developing Affordable Small-Scale Household Sustainable Technologies in Northern Uganda</p> <p>Jens Bo Holm-Nielsen, Achora Proscovia O Mamur, and Samson Masebinu</p> <p> </p> <p>PART VI. Other machine learning applications</p> <p>17. A Parallel Bidirectional Long Short-Term Memory Model for Non-Intrusive Load Monitoring</p> <p>Victor Andrean and Kuo-Lung Lian</p> <p> </p> <p>18. Reinforcement Learning for Intelligent Building Energy Management System Control</p> <p>Olivera Kotevska and Philipp Andelfinger</p> <p> </p> <p>19. Federated Deep Learning Technique for Power and Energy Systems Data Analysis</p> <p>Hamed Moayyed, Arash Moradzadeh, Behnam Mohammadi-Ivatloo, and Reza Ghorbani</p> <p> </p> <p>20. Data Mining and Machine Learning for Power System Monitoring, Understanding, and Impact Evaluation</p> <p>Xinda Ke, Huiying Ren, Qiuhua Huang, Pavel Etingov and Zhangshuan Hou</p> <p> </p> <p>Conclusions</p> <p>Zita Vale, Tiago Pinto, Michael Negnevitsky, and Ganesh Kumar Venayagamoorthy</p>

<p><b>Zita Vale, PhD,</b> 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. <p><b>Tiago Pinto, PhD,</b> 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. <p><b>Michael Negnevitsky, PhD,</b> 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. <p><b>Ganesh Kumar Venayagamoorthy, PhD,</b> 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.

<p><b>A hands-on and current review of data mining and analysis and their applications to power and energy systems</b> <p>In <i>Intelligent Data Mining and Analysis in Power and Energy Systems: Models and Applications for Smarter Efficient Power Systems</i>, 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. <p>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: <ul><li> A thorough introduction to data mining and analysis, including the foundations of data preparation and a review of various analysis models and methods</li> <li> In-depth explorations of clustering, classification, and forecasting</li> <li> Intensive discussions of machine learning applications in power and energy systems</li></ul> <p>Perfect for power and energy systems designers, planners, operators, and consultants, <i>Intelligent Data Mining and Analysis in Power and Energy Systems</i> will also earn a place in the libraries of software developers, researchers, and students with an interest in data mining and analysis problems.

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