Research Article
Performance Analysis of an Optimized ANN Model to Predict the Stability of Smart Grid
Table 3
Accuracy for every fold using repeated random test-train split.
| S.No. | Model | Accuracy Fold1 | Accuracy Fold2 | Accuracy Fold3 | Accuracy Fold4 | Accuracy Fold5 | Accuracy Fold6 | Accuracy Fold7 | Accuracy Fold8 | Accuracy Fold9 | Accuracy Fold10 |
| 1 | Logistic regression | 0.81405 | 0.81527 | 0.8145 | 0.81838 | 0.8146 | 0.81561 | 0.8131 | 0.81116 | 0.81361 | 0.81272 | 2 | KNN | 0.813889 | 0.82283 | 0.81522 | 0.82078 | 0.81656 | 0.81267 | 0.81639 | 0.81561 | 0.81628 | 0.81789 | 3 | Naïve Bayes | 0.832556 | 0.8315 | 0.82444 | 0.83039 | 0.83056 | 0.83256 | 0.83239 | 0.82828 | 0.83311 | 0.83156 | 4 | Decision tree | 0.83305 | 0.83156 | 0.83183 | 0.837 | 0.83328 | 0.83244 | 0.83178 | 0.8315 | 0.8345 | 0.83139 | 5 | SVM | 0.927667 | 0.92706 | 0.92622 | 0.92944 | 0.92806 | 0.92628 | 0.92789 | 0.92767 | 0.92844 | 0.92911 | 6 | Random forest | 0.907056 | 0.90939 | 0.90572 | 0.90983 | 0.90928 | 0.909 | 0.90772 | 0.91183 | 0.90817 | 0.91056 | 7 | XGBoost | 0.92983 | 0.92894 | 0.92806 | 0.9275 | 0.93406 | 0.93206 | 0.92817 | 0.93133 | 0.93078 | 0.92794 | 8 | Optimized ANN | 0.9571 | 0.9705 | 0.9724 | 0.9771 | 0.9802 | 0.981 | 0.9781 | 0.9836 | 0.9821 | 0.98 |
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Bold face represents the highest accuracy achieved for every model among different folds.
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