Ensemble Bagged Tree Based Classification for Reducing Non-Technical Losses in Multan Electric Power Company of Pakistan
Abstract
:1. Introduction
2. Classification Using Ensemble Bagged Tree
3. Methodology
3.1. Data Acquisition
3.2. Consumption Pattern of Fraudulent and Honest Consumers
3.3. Customer Filtering and Selection
- All those consumers whose entire 36 months consumption data was not available or those who were not using electricity due to change of residence or any other reason.
- All consumers who were registered after May 2015.
- All healthy consumers who were charged an average i.e., whose metering equipment became defective during the studied time period.
3.4. Studied Classification Methods
3.4.1. Decision Trees
3.4.2. Support Vector Machines (SVM)
3.4.3. K-Nearest Neighbor
3.4.4. Ensemble Classification
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Model | Maximum Number of Splits | Split Criterion |
---|---|---|
Fine Tree | 100 | Gini’s diversity index |
Medium Tree | 20 | Gini’s diversity index |
Coarse Tree | 4 | Gini’s diversity index |
Model | Kernel Function | Kernel Scale |
---|---|---|
Linear SVM | Linear | 1 |
Quadratic SVM | Quadratic | 1 |
Cubic SVM | Cubic | 1 |
Fine Gaussian SVM | Gaussian | 1.5 |
Medium Gaussian SVM | Gaussian | 6 |
Coarse Gaussian SVM | Gaussian | 24 |
Model | Distance Metric | Number of Neighbors |
---|---|---|
Fine KNN | Euclidean | 1 |
Medium KNN | Euclidean | 10 |
Coarse KNN | Euclidean | 100 |
Cosine KNN | Cosine | 10 |
Cubic KNN | Minkowski | 10 |
Weighted KNN | Euclidean | 10 |
Ensemble Models | Ensemble Method | Learner Type | Max. Number of Splits | Number of Learners |
---|---|---|---|---|
Boosted Trees | AdaBoost | Decision tree | 20 | 30 |
Bagged Trees | Bag | Decision tree | 20 | 30 |
Subspace KNN | Subspace | Nearest neighbors | 20 | 30 |
RUSBoosted Trees | RUSBoost Bag | Decision tree | 20 | 30 |
Random Forest | RUSBoost Bag | Decision tree | 20 | 30 |
Model | Accuracy | Sensitivity | Specificity | F1 Score | FPR |
---|---|---|---|---|---|
Decision Trees | |||||
Fine Tree | 85.84 | 42.33 | 99.10 | 29.14 | 0.90 |
Medium Tree | 91.02 | 64.19 | 99.20 | 38.48 | 0.80 |
Coarse Tree | 86.35 | 43.41 | 99.43 | 29.88 | 0.57 |
Support Vector Machines | |||||
Linear SVM (LSVM) | 79.54 | 16.43 | 98.77 | 13.64 | 1.23 |
Quadratic SVM (QSVM) | 85.59 | 52.09 | 95.80 | 31.40 | 4.20 |
Cubic SVM (CSVM) | 52.32 | 58.14 | 50.54 | 18.14 | 49.46 |
Fine Gaussian SVM | 85.01 | 63.88 | 91.45 | 33.28 | 8.55 |
Medium Gaussian SVM | 81.54 | 28.53 | 97.69 | 20.96 | 2.31 |
Coarse Gaussian SVM | 76.94 | 1.86 | 99.81 | 1.82 | 0.19 |
K-Nearest Neighbours | |||||
Fine KNN | 81.79 | 27.29 | 98.39 | 20.58 | 1.61 |
Medium KNN | 79.58 | 13.49 | 99.72 | 11.79 | 0.28 |
Coarse KNN | 76.65 | 0.00 | 100.00 | 0.00 | |
Cosine KNN | 78.20 | 20.00 | 95.94 | 15.00 | 4.06 |
Cubic KNN | 78.96 | 11.01 | 99.67 | 9.82 | 0.33 |
Weighted KNN | 79.33 | 12.40 | 99.72 | 10.94 | 0.28 |
Ensemble Classification | |||||
Boosted Trees | 90.66 | 61.55 | 99.53 | 37.74 | 0.47 |
Bagged Trees | 93.08 | 74.88 | 98.63 | 41.75 | 1.37 |
Subspace Discriminant | 77.23 | 2.79 | 99.91 | 2.71 | 0.09 |
Subspace KNN | 82.80 | 29.61 | 99.01 | 22.29 | 0.99 |
RUSBoosted Tree | 89.03 | 66.51 | 95.89 | 36.95 | 4.11 |
Random Forest | 90.03 | 67.51 | 96.89 | 37.71 | 3.11 |
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Saeed, M.S.; Mustafa, M.W.; Sheikh, U.U.; Jumani, T.A.; Mirjat, N.H. Ensemble Bagged Tree Based Classification for Reducing Non-Technical Losses in Multan Electric Power Company of Pakistan. Electronics 2019, 8, 860. https://doi.org/10.3390/electronics8080860
Saeed MS, Mustafa MW, Sheikh UU, Jumani TA, Mirjat NH. Ensemble Bagged Tree Based Classification for Reducing Non-Technical Losses in Multan Electric Power Company of Pakistan. Electronics. 2019; 8(8):860. https://doi.org/10.3390/electronics8080860
Chicago/Turabian StyleSaeed, Muhammad Salman, Mohd Wazir Mustafa, Usman Ullah Sheikh, Touqeer Ahmed Jumani, and Nayyar Hussain Mirjat. 2019. "Ensemble Bagged Tree Based Classification for Reducing Non-Technical Losses in Multan Electric Power Company of Pakistan" Electronics 8, no. 8: 860. https://doi.org/10.3390/electronics8080860