Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach
<p>Block diagram of the proposed electricity theft detection system.</p> "> Figure 2
<p>Fortnightly electricity power consumption pattern.</p> "> Figure 3
<p>Distribution of normal and theft users in the dataset.</p> "> Figure 4
<p>Distribution of normal and theft users after applying synthetic minority over-sampling technique (SMOTE).</p> "> Figure 5
<p>Long short-term memory (LSTM) architecture.</p> "> Figure 6
<p>Proposed convolutional neural network based LSTM (CNN-LSTM) model.</p> "> Figure 7
<p>Confusion matrix.</p> "> Figure 8
<p>Effect of weekly, fortnightly, and monthly consumption patterns on the model. (<b>a</b>) Precision; (<b>b</b>) Recall; (<b>c</b>) F1 Score; (<b>d</b>) Accuracy.</p> "> Figure 9
<p>(<b>a</b>) Loss, (<b>b</b>) Matthews correlation coefficient (MCC), and (<b>c</b>) accuracy for the unbalanced dataset.</p> "> Figure 10
<p>(<b>a</b>) Loss, (<b>b</b>) MCC, and (<b>c</b>) accuracy for the balanced dataset.</p> "> Figure 11
<p>Confusion matrices for the imbalanced and balanced dataset.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Electricity Theft Data
2.2. Data Preprocessing
2.2.1. Computing the Missing Values
2.2.2. Generation of Synthetic Data Points
Algorithm 1: Data Pre-processing Algorithm |
|
2.3. Proposed CNN-LSTM Architecture for Smart Grid Data Classification
2.3.1. CNN Model
2.3.2. LSTM
3. Results and Discussion
3.1. Performance Matrices
3.1.1. Binary Cross-Entropy
3.1.2. Matthews Correlation Coefficient (MCC)
3.1.3. F-measure
3.2. Experimental Results
Performance Comparison
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Description | Value |
---|---|
Time window of data collection | 1 January 2015–31 December 2015 |
Total number of consumers | 9956 |
Number of normal users | 8562 |
Number of aberrant user or electricity thieves | 1394 |
Parameters | Before applying SMOTE | After applying SMOTE | |
---|---|---|---|
Precision | Normal user | 0.92 | 0.90 |
Theft user | 0.62 | 0.87 | |
Recall | Normal user | 0.96 | 0.87 |
Theft user | 0.45 | 0.91 | |
F1-Score | Normal user | 0.94 | 0.89 |
Theft user | 0.52 | 0.89 | |
Overall Accuracy | 0.89139 | 0.8882 |
Methods | Precision | Recall | F1-Score | Accuracy |
---|---|---|---|---|
Logistic regression | 0.7 | 0.69 | 0.69 | 69% |
SVM | 0.79 | 0.79 | 0.78 | 79% |
CNN-LSTM | 0.92 | 0.96 | 0.94 | 89% |
# | Reference | Techniques applied | Number of Customers | Accuracy (%) | Precision | Recall | Source |
---|---|---|---|---|---|---|---|
1 | [20] | DT coupled SVM | NA | 92.50 | NA | NA | |
2 | [34] | Combination functions (SVM, OPF, C4,5 tree) | NA | 86.20 | 54.4 | 64 | Uruguayan Electric Company (UTE) |
3 | [20] | Regression | 30 | 78 | NA | 1 | Electricity Thefts Surge in Bad Times,” March 16, 2009, USA Today, via Factiva, © 2009 USA Today. |
5 | [33] | SVM | 36176 | 60 | NA | NA | Tenaga Nasional Berhad Distribution (TNBD), Sdn. Bhd. |
6 | [13] | SVM-based fraud detection model (FDM) with the introduction of a fuzzy inference system (FIS)- SVM-FIS | 36176 | 72 | NA | NA | Tenaga Nasional Berhad Distribution (TNBD), Sdn. Bhd. |
7 | [36] | Genetic- SVM | 186,968 | 62 | NA | NA | Tenaga Nasional Berhad Distribution (TNBD), |
8 | [13] | SVM -fuzzy | 36173 | 72 | NA | NA | Tenaga Nasional Berhad Distribution (TNBD) |
9 | [54] | Fuzzy logic | 55 | NA | NA | TNB Metering Services database | |
10 | [41] | Fuzzy classification | 74.50 | NA | NA | brazil | |
11 | [55] | Neural networks (NN) | 83.5 | 24.929.8 | Light S.A. Company, Brazil | ||
12 | [56] | Neuro-fuzzy | 4159 | 68.2 | 51.2 | Light S.A. Company, Brazil | |
13 | [57] | Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and Stacked Autoencoder. | 12,180 | 96.9 | NA | NA | |
14 | [7] | Wide and Deep CNN | 42372 | 94.04 | NA | State Grid Corporation of China (SGCC) | |
15 | This work | Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) | 17120 | 89 | 0.90 | 0.87 |
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Hasan, M.N.; Toma, R.N.; Nahid, A.-A.; Islam, M.M.M.; Kim, J.-M. Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach. Energies 2019, 12, 3310. https://doi.org/10.3390/en12173310
Hasan MN, Toma RN, Nahid A-A, Islam MMM, Kim J-M. Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach. Energies. 2019; 12(17):3310. https://doi.org/10.3390/en12173310
Chicago/Turabian StyleHasan, Md. Nazmul, Rafia Nishat Toma, Abdullah-Al Nahid, M M Manjurul Islam, and Jong-Myon Kim. 2019. "Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach" Energies 12, no. 17: 3310. https://doi.org/10.3390/en12173310