Intelligent Data Mining and Analysis in Power and Energy Systems Models and Applications for Smarter Efficient Power Systems

by Vale , Zita A.; Pinto, Tiago; Negnevitsky, Michael; Venayagamoorthy, Ganesh Kumar

• ISBN13: 9781119834021
• ISBN10: 1119834023
• Edition: 1st
• Format: Hardcover
• Copyright: 2022-12-28
• Publisher: Wiley-IEEE Press

Summary

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.

Author Biography

Zita A. Vale 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.

Tiago Pinto is an artificial intelligence expert who focuses on adaptive machine learning and automated negotiation. He has written over 200 publications and co-edited several books.

Michael Negnevitsky, PhD, is an Adjunct Full Professor in the School of Engineering at the University of Vermont in Burlington, USA. He is also Honorary Professor of Shandong University of Science and Technology in China.

Ganesh Kumar Venayagamoorthy is the Duke Energy Distinguished Professor of Electrical and Computer Engineering at Clemson University. He is a Senior Member of the IEEE and International Neural Network Society.

Table of Contents

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