Electricity Theft Detection Techniques
Electricity Theft Detection Techniques is a research topic within Electrical and Electronic Engineering. Science Explorer counts 6k research works in it since 1953. 15.0% of them reached the world's top 10% most cited for their field and year.
This cluster of papers focuses on the detection and prevention of electricity theft in smart grids, particularly through the use of advanced metering infrastructure, machine learning, deep learning, and anomaly detection techniques. The research explores methods such as support vector machines, decision trees, convolutional neural networks, and feature engineering to address non-technical losses and improve the security of electricity distribution systems.
- Smart Grids
- Electricity Theft
- Detection
- Advanced Metering Infrastructure
- Non-Technical Losses
- Machine Learning
- Deep Learning
- Feature Engineering
- Supervised Learning
- Anomaly Detection
- Research works
- 6k fractional, since 1953
- In the world top 10%
- 900 per year above
- Top-10% rate
- 15.0% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- +177% the tick is no change
Which countries lead Electricity Theft Detection Techniques research?
By volume, China and India publish the most (644 and 416 works in 2022–2025).
By volume, 2022–2025
- 1 China 644 works
- 2 India 416 works
- 3 Brazil 177 works
- 4 United States 168 works
- 5 Indonesia 85 works
- 6 Türkiye 51 works
- 7 ?? 45 works
- 8 Iran 44 works
- 9 Nigeria 41 works
- 10 United Kingdom 40 works
How concentrated that is
The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.
Shares of the rows listed above, not of the whole node.
Which institutions lead Electricity Theft Detection Techniques research?
By volume in 2022–2025, State Grid Corporation of China (China) publishes the most Electricity Theft Detection Techniques research, followed by Shanghai Electric (China) and China Southern Power Grid (China).
By volume, 2022–2025
- 1 State Grid Corporation of China (China) China 53 works
- 2 Shanghai Electric (China) China 36 works
- 3 China Southern Power Grid (China) China 31 works
- 4 North China Electric Power University China 19 works
- 5 Power Grid Corporation (India) India 15 works
- 6 Universidade Federal de Santa Maria Brazil 12 works
- 7 Amrita Vishwa Vidyapeetham India 10 works
- 8 Universidade Estadual Paulista (Unesp) Brazil 10 works
- 9 China Electric Power Research Institute ?? 10 works
- 10 South China University of Technology China 10 works
Who are the leading researchers in Electricity Theft Detection Techniques?
The most-cited researchers publishing on Electricity Theft Detection Techniques include Francisco Herrera, MengChu Zhou and Mohammad Shahidehpour.
- 1 Francisco Herrera 6.5k citations
- 2 MengChu Zhou 4.4k citations
- 3 Mohammad Shahidehpour 3.3k citations
- 4 Taghi M. Khoshgoftaar 2.3k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Electricity Theft Detection Techniques research done?
The largest centres of Electricity Theft Detection Techniques research in 2022–2025 are Beijing (China), Shanghai (China), Guangzhou (China) and Chennai (India). Among places with at least 20 works in it, it is an unusually large share of all research in Coimbatore.
Largest cities, 2022–2025
Where it is the local speciality
- CoimbatoreIN · 22.1 works5.4×
Location quotient: how much more of its research is in Electricity Theft Detection Techniques than the world average.
Where is the best place to study Electricity Theft Detection Techniques?
Among universities, judged by research, Amrita Vishwa Vidyapeetham, North China Electric Power University and University of Johannesburg score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Amrita Vishwa Vidyapeetham India | 56.7 | 31.9% | 8.3× | 10 | — |
| 2 | North China Electric Power University China | 45.0 | 8.2% | 12.1× | 19 | +4.6% |
| 3 | University of Johannesburg South Africa | 44.6 | 22.4% | 6.4× | 9 | — |
| 4 | South China University of Technology China | 41.0 | 30.0% | 3.7× | 10 | — |
| 5 | Hunan University China | 40.2 | 22.7% | 5.6× | 9 | — |
| 6 | Xi'an Jiaotong University China | 40.1 | 35.1% | 2.4× | 9 | +33.7% |
| 7 | Universidade Federal de Santa Maria Brazil | 39.1 | 0.0% | 12.6× | 12 | +93.3% |
| 8 | Chongqing University China | 36.8 | 26.3% | 3.5× | 9 | — |
| 9 | Vellore Institute of Technology University India | 31.7 | 21.7% | 3.6× | 8 | — |
| 10 | Tianjin University China | 29.5 | 19.5% | 3.1× | 9 | — |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Electricity Theft Detection Techniques research growing?
Output in 2018–2022 was 177% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Electricity Theft Detection Techniques.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Which topics inside it are moving
Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.