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.
Table of Content
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
About the author
Zita Vale, Ph D, 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, Ph D, 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, Ph D, 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, Ph D, 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.
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