Dealing with Demand in Electric Grids with an Adaptive Consumption Management Platform

Table 2

Datasets results.

Algorithm	Four classes’ dataset		Seven classes’ dataset		Ten classes’ dataset
	Accuracy	Kappa	Accuracy	Kappa	Accuracy	Kappa
RIPPER	0.9775	0.97	0.967143	0.9617	0.928	0.92
PART	0.9875	0.9833	0.975714	0.9717	0.959	0.9544
C4.5	0.9875	0.9833	0.974286	0.97	0.958	0.9533
RandomForest	0.9925	0.99	0.987143	0.985	0.98	0.9778
RandomTree	0.975	0.9667	0.977143	0.9733	0.96	0.9556
REPTree	0.99	0.9867	0.978571	0.975	0.945	0.9389
-NN	0.97	0.96	0.967143	0.9617	0.952	0.9467
kStart	0.9575	0.9633	0.972857	0.9683	0.966	0.9622
Bayesian networks	0.9575	0.9434	0.94	0.93	0.927	0.9189
Neural networks	0.8875	0.85	0.924286	0.9117	0.853	0.8367