# Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter
## 4 Experimental Results  
The experiments were performed on a 64-bit operating system with 8 GB RAM, 4 cores and an Intel Core i7-8565U CPU processor. The time needed for the execution of the Decision Tree classifier was 2 min on average. This was also the case for the Random forest classifier. On the contrary, the Multilayer Perceptron classifier needed 5 min on average. Our experimental design is based on trying out subsets of the set of acquired features in order to compare them with the full set with respect to their classification effectiveness. In particular, for the Decision Tree and Random Forest classifiers we used only active power compared to the full feature set while for the Multilayer Perceptron Classifier, we used both active and reactive power since otherwise the results were quite poor. In all cases, the classification effectiveness of the classifiers was worse that the case where all the features are used. The results can be found in Tables 1-7. In our experiments, we notice that most of the measurements that were classified wrongly were those in the transient phase, that is during the first milliseconds of the operation of the appliance. The measurements during the transient phase are not easy to classify. However, by adding more features in the training phase, we manage to wrongly classify less measurements during the transient phase. This means that the additional information provided in the form of additional features helps in correctly identifying the operating appliance. In the following, we discuss the results for each appliance. For the vacuum cleaner (see Table 1), both for the Decision Tree classifier and the Random Forest classifier, the results are improved considerably when using the whole set  
Figure 18: Press’ behavior. The ironing press operated in a time period where no other device changed the state. of features instead of the active power alone. However, the improvement in the Multilayer Perceptron classifier was only marginal. This is due to two reasons: on the one hand, this classifier compares active-reactive to the whole set of features and on the other hand the vacuum cleaner has one mode of operation and thus it is easy for this classifier to identify the measurements. The improvement of the classification results were mainly located at the transient phase. Regarding the electrical oven, its behavior is similar to the behavior of the stove and as a result we discuss them together (see Tables 2 and 3). It is more difficult to correctly classify measurements from those devices due to the many changes in the power consumption. Furthermore, the Multilayer Perceptron classifier has surprisingly less accurate predictions than the rest of the classifiers. Yet, we can observe that with the extra features, the results are noticeably better, for all the classifiers. Once more, we observed that the improvement in the classification results when using all features was mainly located in the transient phase. As for the ironing press and the air-conditioner, the differences between implementing the training with only active power and implementing the training with the whole set of features are shown in Tables 4 and 5 and are obviously better for the latter set of features. For the ironing press we delve deeper by looking at various subsets of features. The results can be found in Tables 6 and 7. In general, when adding more features to the training phase, the classifiers return less false negatives and false positives. For example, when using active power, reactive power and crest factor, the metrics return better values, although they can be further enhanced. In the case of considering four features, such as active power,