# Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring
## 8 Conclusions  
This study presents the effectiveness of NCA as a feature selection method to enhance the classification performance of a multilayer perceptron for non-intrusive load monitoring. Two feature selection methods, namely the neighborhood component analysis and minimum redundancy maximum relevance algorithms, have been applied to a set of sixteen features including active fundamental and harmonic active power and fundamental reactive power in four windows successive to a switching event. The results reveal that NCA feature selection method can select an optimal set of features tackling the model complexity. Indeed, the top features of the algorithm, fundamental active and reactive power and active power harmonics, allows one to obtain the best MLP performance also reducing the model complexity. Comparisons with results presented in literature show that the procedure achieves competitive performance despite the model simplicity.  
Conceptualization, B.C., S.C., D.C. and C.M.; methodology, B.C., S.C., D.C. and C.M.; software, S.C. and D.C.; validation, B.C., S.C. and D.C.; formal analysis, B.C. and S.C.; investigation, B.C., S.C. and D.C.; resources, S.C. and D.C.; data curation, S.C. and D.C.; writing--original draft preparation, S.C. and D.C.; writing--review and editing, B.C., S.C., D.C. and C.M.; visualization, B.C. and S.C.; supervision, B.C., C.M. and A.F.; project administration, C.M.; funding acquisition, C.M. and B.C. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **[22]** & \multicolumn{2}{c}{**MLP**} \\ \hline
**Appliance** & **\# Events in 24 h** & **F-Score** & **\# Events in One Week** & **F-Score** \\ \hline Fridge & 99 & 99.5 & 546 & 99.54 \\ Air Compressor & 2 & 100 & 20 & 100 \\ Backyard lights & 2 & 66.67 & 16 & 91.43 \\ Bathroom upstairs light & 16 & 94.12 & 97 & 93 \\ Bedroom lights & 6 & 85.71 & 18 & 88.24 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with the performance reported in [22] for five appliances of BLUED.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Precision** & **Recall** & **F-Score** \\ \hline
0.89 & 0.90 & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance reported in [24] for random forest algorithms applied to BLUED dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Precision** & **Recall** & **F-Score** \\ \hline
0.90 & 0.81 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance reported in [9] for concatenate convolutional neural networks applied to six classes of devices in BLUED dataset.  
**Funding:** This research was funded by Sardinian Region under project \"RT-NILM (Real-Time Non-Intrusive Load Monitoring for intelligent management of electrical loads)\", Call for basic research projects, year 2017, FSC 2014-2020.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.