Joint Feature Selection of Power Load in Time Domain and Frequency Domain Based on Whale Optimization Algorithm

Table 2

Classification results of original power signals.

	Classifier	Test 1	Test 2	Test 3	Test 4	Test 5	Mean value	Standard deviation
Accuracy (%)	BP	39.44	42.36	49.51	44.75	43.01	43.81	3.72
	ELM	38.14	40.85	45.39	41.82	41.28	41.50	2.60
	SVM	46.56	46.72	46.30	45.91	46.20	46.34	0.32
	KNN	25.24	25.79	25.46	27.95	23.84	25.66	1.48
	DT	49.14	49.14	49.14	49.14	49.14	49.14	0.00
	NB	73.44	73.44	73.44	73.44	73.44	73.44	0.00
Time (s)	BP	227.1572	217.4501	222.0936	222.8179	218.7004	221.6438	3.8154
	ELM	0.4152	0.4418	0.4476	0.4367	0.4139	0.4310	0.0155
	SVM	211.3685	222.0861	223.4372	214.3196	216.0319	217.4487	5.1511
	KNN	1.8922	1.9682	1.7896	1.9046	1.8769	1.8863	0.0643
	DT	2.1488	2.1372	2.0935	2.1837	2.0789	2.1284	0.0425
	NB	4970.3256	4975.1241	4961.7400	4950.3201	4958.9041	4963.2828	9.7391
Description of core parameters of the classifier	(1) : represents the number of hidden layer neurons in BP.
	(2) : represents the number of hidden layer neurons in ELM.
	(3) : represents penalty parameter, and represents the parameter of kernel function.
	(4) : represents the number of nearest neighbors in the input for classifying each point when predicting.
	(5) : represents maximal number of decision splits (or branch nodes) per tree.
	(6) : represents the distribution used to model the data, and “kernel” refers to kernel smoothing density estimate.