Intelligent Data Mining and Analysis in Power and Energy Systems 1st Edition by Zita Vale, Tiago Pinto, Michael Negnevitsky, Ganesh Kumar Venayagamoorthy ISBN 9781119834045 111983404X

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Product details:
ISBN 10: 111983404X
ISBN 13: 9781119834045
Author: Zita Vale, Tiago Pinto, Michael Negnevitsky, Ganesh Kumar Venayagamoorthy

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.

Intelligent Data Mining and Analysis in Power and Energy Systems 1st Edition Table of contents:

Part I: Data Mining and Analysis Fundamentals

1 Foundations

1.1 Data Mining: Why and What?

1.2 Data Mining into KDD

1.3 The Data Mining Process

1.4 Data Mining Task and Techniques

1.5 Data Mining Issues and Considerations

1.6 Summary

References

2 Data Mining and Analysis in Power and Energy Systems: An Introduction to Algorithms and Applications*

Acronyms

2.1 Introduction

2.2 Data Mining Technologies

2.3 Data Mining Applications in Power Systems

2.4 Discussion and Final Remarks

References

Note

3 Deep Learning in Intelligent Power and Energy Systems

3.1 Introduction

3.2 Deep Learning

3.3 Accomplishments, Limitations, and Challenges

3.4 Conclusions

References

Part II: Clustering

4 Data Mining Techniques Applied to Power Systems

4.1 Introduction

4.2 Data Mining Techniques

4.3 Data Mining Techniques Applied to Power Systems

4.4 Electrical Tariffs Design Based on Data Mining Techniques

4.5 Data Mining Contributions to Characterize Zonal Prices

4.6 Data Mining‐Based Methodology for Wind Forecasting

4.7 Final Remarks

References

5 Synchrophasor Data Analytics for Anomaly and Event Detection, Classification, and Localization

5.1 Introduction

5.2 Synchrophasor Data Quality Issues and Challenges

5.3 ML‐Based Anomaly Detection, Classification, and Localization (ADCL) Over Data Drifting Multivariate Synchrophasor Data Streams

5.4 Synchrophasor Data Anomaly and Event Detection, Localization, and Classification (SyncAED)

5.5 Test‐Bed and Test Cases

5.6 Results and Discussion

5.7 Summary

Acknowledgments

References

6 Clustering Methods for the Profiling of Electricity Consumers Owning Energy Storage System

6.1 Introduction

6.2 Methodology Definition

6.3 Clustering of Consumers with ESS

6.4 Conclusion

Acknowledgments

References

Part III: Classification

7 A Novel Framework for NTL Detection in Electric Distribution Systems

7.1 Introduction

7.2 Data Acquisition and Pre‐Processing

7.3 Feature Extraction

7.4 Classification Strategies

7.5 Evaluation

7.6 Experiments

7.7 Conclusion

References

8 Electricity Market Participation Profiles Classification for Decision Support in Market Negotiation

8.1 Introduction

8.2 Bilateral Negotiation

8.3 Decision Support for Bilateral Negotiations

8.4 Illustrative Results

8.5 Conclusion

References

9 Socio‐demographic, Economic, and Behavioral Analysis of Electric Vehicles

9.1 Introduction

9.2 Electric Vehicle Outlook

9.3 Data Mining Models for EVs

9.4 Conclusions

References

Part IV: Forecasting

10 A Multivariate Stochastic Spatiotemporal Wind Power Scenario Forecasting Model

10.1 Introduction

10.2 Generalized Dynamic Factor Model

10.3 Conclusion

References

11 Spatiotemporal Solar Irradiance and Temperature Data Predictive Estimation

11.1 Introduction

11.2 Virtual Weather Stations

11.3 Distributed Weather Forecasting

11.4 Results and Discussion

11.5 Summary

Acknowledgment

References

12 Application of Decomposition‐Based Hybrid Wind Power Forecasting in Isolated Power Systems with High Renewable Energy Penetration

12.1 Introduction

12.2 Decomposition Techniques

12.3 Decomposition‐Based Neural Network Forecasting

12.4 Forecast‐Based Dispatch in Isolated Power Systems

12.5 Case Studies

12.6 Conclusions and Discussions

References

Part V: Data Analysis

13 Harmonic Dynamic Response Study of Overhead Transmission Lines

13.1 Introduction to Methodology

13.2 Problem Formulation

13.3 Numerical Analysis

13.4 Conclusion

13.A Appendix

References

14 Evaluation of Shortest Path to Optimize Distribution Network Cost and Power Losses in Hilly Areas: A Case Study

14.1 Introduction

14.2 Design of Power Distribution Network

14.3 Digital Elevation Map

14.4 Placement of Generators and Load Centers

14.5 Single Line Diagram of 9‐Bus System

14.6 Finding Shortest Path Between Load/Generating Centers

14.7 Selection of Conductor Using Newton Raphson Method

14.8 Calculation of CO2 Emission Cost Saving

14.9 Overall Cost Estimation of Distribution System

14.10 Sensitivity Analysis

14.11 Conclusion

References

15 Intelligent Approaches to Support Demand Response in Microgrid Planning

15.1 Introduction

15.2 Microgrid Planning

15.3 Demand Response in Microgrids

15.4 Intelligent Approaches to Support Demand Response

15.5 Conclusion

References

16 Socioeconomic Analysis of Renewable Energy Interventions: Developing Affordable Small‐scale Household Sustainable Technologies in Northern Uganda

16.1 Introduction

16.2 Renewable Energy Technologies

16.3 Methodology

16.4 Application of the Method

16.5 Case Study Results for Product Development

16.6 Cost–Benefit Analysis (CBA)

16.7 Conclusion

References

Note

Part VI: Other Machine Learning Applications

17 Non‐Intrusive Load Monitoring Using A Parallel Bidirectional Long Short‐Term Memory Model

17.1 Introduction

17.2 NILM System and Data Preprocessing

17.3 Proposed Method

17.4 Validation

17.5 Conclusion

References

Note

18 Reinforcement Learning for Intelligent Building Energy Management System Control*

Chapter Objectives

18.1 Introduction

18.2 Reinforcement Learning

18.3 Applications of Deep Reinforcement Learning in Building Energy Management Systems Control

18.4 Challenges and Research Directions

18.5 Conclusions

References

Note

19 Federated Deep Learning Technique for Power and Energy Systems Data Analysis

Nomenclature

19.1 Introduction

19.2 Federated Learning (FL)

19.3 Power Systems Challenges and the Performance of Artificial Intelligence Techniques in It

19.4 Application of Federated Deep Learning in Power and Energy Systems

19.5 Conclusion

References

20 Data Mining and Machine Learning for Power System Monitoring, Understanding, and Impact Evaluation

20.1 Introduction

20.2 Power System Monitoring with Phasor Measurement Unit Data

20.3 Power System Mechanistic and Predictive Understanding

20.4 Characterization and Modelling of Weather and Power Extremes

20.5 Conclusion

References

Conclusions

Index

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