Intelligent Data Mining and Analysis in Power and Energy Systems (eBook)

Zita Vale, PhD, 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, PhD, 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, PhD, 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, PhD, 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.

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