A Machine Learning-Based Model for Stability Prediction of Decentralized Power Grid Linked with Renewable Energy Resources

Table 5

Performance of ML models on an undersampled dataset.

Undersampling technique	Models	Accuracy (%)	Precision (%)	Recall (%)	-measure (%)	ROC
Cluster centroids	ANN	94.4	94.6	94.1	94.4	98.7
	Averaged perceptron	79.1	79.5	78.6	79	88.1
	Bayes point machine	79.1	79.4	78.7	79	88.1
	Decision forest	91.1	93.3	88.6	90.9	97.1
	Decision jungle	88.9	88.8	89.1	88.9	95.8
	GBDT	94.2	94.4	94.1	94.2	98.9
	LightGBM	93.6	94.2	92.8	93.5	98.4
	Locally deep SVM	90.9	91.1	90.6	90.9	97.2
	LR	79.1	79.6	78.4	79	88.1
	SVM	78.5	78.9	77.9	78.4	87.5
	XGBoost	93.7	94.1	93.3	93.7	98.7
Near miss	ANN	92	73.2	89.8	91.9	98.1
	Averaged perceptron	75.2	74	77.9	75.9	83.1
	Bayes point machine	75.1	74.1	77.5	75.7	83
	Decision forest	91.1	93.3	88.6	90.9	97.1
	Decision jungle	85.3	83.7	87.7	85.7	93.1
	GBDT	92.1	90.6	94	92.3	98.3
	LightGBM	91.8	90.6	93.2	91.9	97.9
	Locally deep SVM	88.3	88.3	88.3	88.3	94.6
	LR	75.2	73.9	78	75.9	83
	SVM	74.6	73.2	77.6	75.3	82.3
	XGBoost	92.6	92.1	93.1	92.6	98.8
Random undersampling	ANN	94.5	97.8	91	94.3	99
	Averaged perceptron	80.2	80.1	80.4	80.3	88.9
	Bayes point machine	79.7	79.7	79.9	79.8	88.8
	Decision forest	89	89.1	89	89	95.9
	Decision jungle	89.2	87.8	91.1	89.4	95.9
	GBDT	94.1	94.5	93.7	94.1	98.8
	LightGBM	92.7	93.1	92.1	92.6	98.3
	Locally deep SVM	90	89.8	90.2	90	96.6
	LR	79.8	79.7	80	79.9	88.7
	SVM	80.3	80	80.9	80.4	89
	XGBoost	93.4	93.6	93.1	93.3	98.7