Intelligent Data Mining and Analysis in Power and Energy Systems

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 methods In-depth explorations of clustering, classification, and forecasting Intensive discussions of machine learning applications in power and energy systems Perfect 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.

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