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Topic · Electrical and Electronic Engineering

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. 10.2% 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%
609
per year above
Top-10% rate
10.2%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+65%
the tick is no change
Largest producer
China
39% of the 10 largest, 2022-now
Electricity Theft Detection Techniques

Which countries lead Electricity Theft Detection Techniques research?

By volume, China and India publish the most (777 and 491 works in 2022-now).

By volume, 2022-now

  1. 1 China 777 works
  2. 2 India 491 works
  3. 3 United States 195 works
  4. 4 Brazil 190 works
  5. 5 Indonesia 98 works
  6. 6 Türkiye 59 works
  7. 7 ?? 52 works
  8. 8 Nigeria 49 works
  9. 9 Iran 49 works
  10. 10 United Kingdom 47 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.

China: 38.8%India: 24.5%United States: 9.7%Brazil: 9.5%6 others listed: 17.6%39%largest
China777 · 38.8%India491 · 24.5%United States195 · 9.7%Brazil190 · 9.5%6 others listed353 · 17.6%

Shares of the rows listed above, not of the whole node.

Which institutions lead Electricity Theft Detection Techniques research?

By volume in 2022-now, 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-now

  1. 1 State Grid Corporation of China (China)China 77 works
  2. 2 Shanghai Electric (China)China 42 works
  3. 3 China Southern Power Grid (China)China 39 works
  4. 4 North China Electric Power UniversityChina 25 works
  5. 5 Power Grid Corporation (India)India 21 works
  6. 6 Universidade Federal de Santa MariaBrazil 12 works
  7. 7 Amrita Vishwa VidyapeethamIndia 12 works
  8. 8 Xi'an Jiaotong UniversityChina 11 works
  9. 9 Universidade Estadual Paulista (Unesp)Brazil 11 works
  10. 10 Hunan UniversityChina 10 works

Who are the leading researchers in Electricity Theft Detection Techniques?

The most-cited researchers publishing on Electricity Theft Detection Techniques include H. Vincent Poor, Francisco Herrera and Josep Maria Guerrero.

  1. 1 H. Vincent Poor United States 165k citations
  2. 2 Francisco Herrera Spain 141k citations
  3. 3 Josep Maria Guerrero Denmark 123k citations
  4. 4 A. S. Cerqueira France 116k citations
  5. 5 Xuemin Shen Canada 104k citations
  6. 6 Dusit Tao Niyato Singapore 98k citations
  7. 7 Zhu Han United States 96k citations
  8. 8 Witold Pedrycz Canada 95k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

What are the most cited papers in Electricity Theft Detection Techniques?

The most cited paper in Electricity Theft Detection Techniques is "Learning from Imbalanced Data" (Haibo He, Edwardo A. Garcia, 2009), with 10,349 citations. Next come "A systematic study of the class imbalance problem in convolutional neural networks" (3,120) and "Survey on deep learning with class imbalance" (3,018).

Most cited papers
#PaperReleasedCitations
1 Learning from Imbalanced Data Haibo He, Edwardo A. Garcia · IEEE Transactions on Knowledge and Data Engineering Jul 2009 10,349
2 A systematic study of the class imbalance problem in convolutional neural networks Mateusz Buda, Atsuto Maki, Maciej A. Mazurowski · Neural Networks Open access 29 Jul 2018 3,120
3 Survey on deep learning with class imbalance Justin M. Johnson, Taghi M. Khoshgoftaar · Journal Of Big Data Open access 19 Mar 2019 3,018
4 A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches Mikel Galar, Alberto Fernández, Edurne Barrenechea et al. · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) 15 Aug 2011 2,851
5 Exploratory Undersampling for Class-Imbalance Learning Xuying Liu, Jianxin Wu, Zhihua Zhou · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) 18 Dec 2008 2,514
All 99 most cited papers, with filters and sorting

Where is Electricity Theft Detection Techniques research done?

The largest centres of Electricity Theft Detection Techniques research in 2022-now 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 Gurgaon.

Largest cities, 2022-now

  1. 1 BeijingChina 155 works
  2. 2 ShanghaiChina 73 works
  3. 3 GuangzhouChina 62 works
  4. 4 ChennaiIndia 44 works
  5. 5 NanjingChina 40 works
  6. 6 BengaluruIndia 30 works
  7. 7 ChangshaChina 29 works
  8. 8 Xi'anChina 26 works
  9. 9 CoimbatoreIndia 26 works
  10. 10 WuhanChina 24 works

Where it is the local speciality

  1. GurgaonIN · 21.5 works19×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Electricity Theft Detection Techniques than the world average.

See Electricity Theft Detection Techniques on the map

Where is the best place to study Electricity Theft Detection Techniques?

Among universities, judged by research, Xi'an Jiaotong University, Hunan University and South China University of Technology 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.

0%10%20%mean 15.23%fractional works in this field (log) →share of work in the world top 10% →Xi'an Jiaotong Unive…South China Universi…Hunan UniversityNorth China Electric…
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Xi'an Jiaotong UniversityChina 63.423.8%2.5×11 +146.0%
2Hunan UniversityChina 61.620.1%5.9×10 +36.7%
3South China University of TechnologyChina 60.923.5%3.8×10 +151.1%
4North China Electric Power UniversityChina 55.53.2%15.0×25 +102.1%
5University of JohannesburgSouth Africa 54.916.1%6.5×10 +79.7%
6Amrita Vishwa VidyapeethamIndia 46.711.2%9.4×12 +248.8%
7Southeast UniversityChina 46.117.3%2.6×10 +89.6%
8Chongqing UniversityChina 44.614.0%3.8×10 +105.2%
9Huazhong University of Science and TechnologyChina 41.611.3%2.1×9 +135.3%
10Tianjin UniversityChina 41.311.8%3.2×10 +129.2%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Electricity Theft Detection Techniques, country by country

Is Electricity Theft Detection Techniques research growing?

Output in 2022-2025 was 65% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Electricity Theft Detection Techniques.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Electricity Theft Detection Techniques as a percentage of every work published that year, 0.010% in 2024.

still counting0.0050%0.010%0.015%Electricity Theft Detection Techniques0.014% in 202519801990200020102020

Fractional works of Electricity Theft Detection Techniques over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Electricity Theft Detection Techniques10.0% in 202520102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

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.

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