# Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Vacuum**} & \multicolumn{2}{c}{**Decision Tree Classifier**} & \multicolumn{2}{c}{**Random Forest Classifier**} & \multicolumn{2}{c}{**Multilayer Perceptron Classifier**} \\ \cline{2-6}  & **A** & **A+** & **A** & **A+** & **AR** & **A+** \\ \hline Accuracy & 0.974 & 0.9999 & 0.9738 & 0.9999 & 0.9994 & 0.9998 \\ Recall & 0.9023 & 0.9997 & 0.9292 & 0.9999 & 0.9991 & 0.9993 \\ Precision & 0.9312 & 0.9998 & 0.9046 & 0.9995 & 0.9979 & 1 \\ F-Measure & 0.9165 & 0.9998 & 0.9167 & 0.9997 & 0.9986 & 0.9996 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results for the vacuum cleaner. With “A” we refer to the experiments that used only active power. With “AR” we refer to experiments with active and reactive power and with “A+” we refer to experiments with all the selected attributes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Oven**} & \multicolumn{2}{c}{**Decision Tree Classifier**} & \multicolumn{2}{c}{**Random Forest Classifier**} & \multicolumn{2}{c}{**Multilayer Perceptron Classifier**} \\ \cline{2-6}  & **A** & **A+** & **A** & **A+** & **AR** & **A+** \\ \hline Accuracy & 0.9804 & 0.9902 & 0.9713 & 0.9907 & 0.8791 & 0.8891 \\ Recall & 0.9447 & 0.9865 & 0.9043 & 0.9565 & 0.3206 & 0.3497 \\ Precision & 0.9267 & 0.9516 & 0.9083 & 0.9843 & 0.7247 & 0.8203 \\ F-Measure & 0.9356 & 0.9687 & 0.9063 & 0.9702 & 0.  
reactive power, RMS current, and crest factor, the results are closer to the optimal. The combination of features that show the best results and can match the results of the whole set of features is active power, reactive power, angle between \(V\&I\), and crest factor. It seems that the ironing press' behavior could be better classified, using only these four parameters. In general, the same behavior was observed in the other appliances as well. Finally, we discuss in detail the distribution of faulty classification measurements in the case of the ironing press. In the case where only active power is considered, the Random Forest classifier had to classify 17.231 records in class \"1\" (ground truth) and returned 17.104 true positives, 127 false negatives and 4476 false positives. A total of 37% of false negatives are located in the transient phases. Furthermore, 97% of false positives are found during the operation of the electric stove. This is because those two devices have \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Press**} & \multicolumn{2}{c}{**Decision Tree Classifier**} & \multicolumn{2}{c}{**Random Forest Classifier**} & \multicolumn{2}{c}{**Multilayer Perceptron Classifier**} \\ \cline{2-7}  & **A, R, Ap** & **A, R, Cr** & **A, R, Ap** & **A, R, Cr** & **A, R, Ap** & **A, R, Cr** \\ \hline Accuracy & 0.9996 & 0.9994 & 0.9996 & 0.9996 & 0.9339 & 0.9511 \\ Recall & 0.9976 & 0.9977 & 0.999 & 0.999 & 0.9981 & 1 \\ Precision & 0.9998 & 0.9985 & 0.9984 & 0.9989 & 0.9999 & 0.7546 \\ F-Measure & 0.9988 & 0.9981 & 0.9987 & 0.999 & 0.9996 & 0.8601 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results from the press’ classifiers with various subsets of features used for training. With “A, R, Ap, Cr” we denote the features Active power, Reactive power, Apparent power, and Crest factor respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**A/C**} & \multicolumn{2}{c}{**Decision Tree Classifier**} & \multicolumn{2}{c}{**Random Forest Classifier**} & \multicolumn{2}{c}{**Multilayer Perceptron Classifier**} \\ \cline{2-7}  & **A** & **A+** & **A** & **A+** & **AR** & **A+** \\ \hline Accuracy & 0.9293 & 0.9997 & 0.9417 & 0.9996 & 0.9339 & 0.9987 \\ Recall & 0.662 & 0.9995 & 0.8994 & 0.999 & 0.6641 & 0.9976 \\ Precision & 0.8574 & 0.9988 & 0.71 & 0.9989 & 0.8907 & 0.9938 \\ F-Measure & 0.7471 & 0.9992 & 0.7936 & 0.999 & 0.7609 & 0.9968 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results for the air-conditioner. With “A” we refer to the experiments that used only active power.