Earticle

초록

영어

This survey explores electricity theft detection in smart grids, where traditional power systems meet modern technology. Smart grids, designed for efficient energy management and continuous integration of renewables, face a pressing challenge electricity theft, costing utility companies over $96 billion annually. The survey traces the evolution from conventional to smart grids, emphasizing their core components. It underscores the economic impact of theft, driving researchers to explore Artificial Intelligence (AI) and Deep Learning (DL) techniques for detection. A comprehensive literature review reveals various approaches, with a focus on DL's growing influence. Public datasets are explored as invaluable resources, and methods for theft detection, including advanced AI and DL, are dissected. Performance metrics like accuracy and precision are discussed, and challenges, including imbalanced data and privacy concerns, are highlighted. In conclusion, the survey emphasizes the need for diverse AI and DL approaches, data sources, and features to create robust theft detection systems for smart grids, ensuring their secure and efficient operation.

목차

Abstract
I. INTRODUCTION
II. BACKGROUND AND LITERATURE REVIEW
III. DEEP LEARNING IN SMART GRIDS
IV. DATA
V. METHODS
VI. PERFORMANCE EVALUATION
VII. CHALLENGES AND LIMITATIONS
VIII. PERSPECTIVES AND FUTURE DIRECTIONS
IX. CONCLUSION
ACKNOWLEDGMENTS:
REFERENCES

키워드

저자

• Waseem Ullah [ Intelligent Media Laboratory, Digital Contents Research Institute, Sejong University, Seoul, South Korea ]
• Altaf Hussain [ Intelligent Media Laboratory, Digital Contents Research Institute, Sejong University, Seoul, South Korea ]
• Muhammad Munsif [ Intelligent Media Laboratory, Digital Contents Research Institute, Sejong University, Seoul, South Korea ]
• Habib Khan [ Intelligent Media Laboratory, Digital Contents Research Institute, Sejong University, Seoul, South Korea ]
• Min Je Kim [ Intelligent Media Laboratory, Digital Contents Research Institute, Sejong University, Seoul, South Korea ]
• Su Min Lee [ Intelligent Media Laboratory, Digital Contents Research Institute, Sejong University, Seoul, South Korea ]
• Myoung Ho Seong [ Sejong University Seoul, Republic of Korea ]
• Sung Wook Baik [ Intelligent Media Laboratory, Digital Contents Research Institute, Sejong University, Seoul, South Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004