## 4 Results and Discussion
### Comparative Performance Between Datasets
#### 4.2.1 F1-Score  
Table 5 summarizes all the measurements for the three datasets, the mean and standard deviation data for each meter, and the application for the F1-score.  
The oZm v3 shows a more variable and generally lower performance than the oZm v1, likely due to the increased number of appliances in its datasets. For example, the oZm v3's F1-score ranges from 0.182 for the vacuum cleaner to 0.609 for the television. In contrast, the oZmv1 consistently delivers a superior performance, particularly for the vacuum cleaner and television, with high mean scores and low variability. It achieves a mean F1-score of 0.707 for the TV and 0.825 for the vacuum cleaner. The OMPM exhibits an intermediate performance with a high variability, achieving lower mean F1-scores than the oZm v1. Its effectiveness varies significantly depending on the appliance and conditions. These findings highlight the importance of selecting the proper meter for an optimal NILM performance.  
The heat map in Figure 9 illustrates the performance of algorithms in identifying the energy consumption through the F1-score. Columns that are greener indicate a better device  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **TV** & **Lamp** &
\begin{tabular}{c} **Vac.** \\ **Cleaner** \\ \end{tabular} & **Fan** & **Freezer** \\ \hline DSUALM10H & Mean & 0.609 & 0.289 & 0.182 & 0.453 & 0.287 \\ (oZm v3) & SD & 0.248 & 0.202 & 0.109 & 0.232 & 0.165 \\ \hline DSUALMH & Mean & 0.707 & No data & 0.825 & 0.664 & 0.578 \\ (oZm v1) & SD & 0.154 & No data & 0.125 & 0.128 & 0.126 \\ \hline UALM2 & Mean & 0.350 & 0.675 & No data & 0.589 & No data \\ (OMPM) & SD & 0.275 & 0.254 & No data & 0.254 & No data \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean and standard deviation data for each meter and application for the F1-score.  
identification, while redder columns suggest difficulties. The performance consistency is evaluated by observing the uniformity or variability in the F1-score within columns.  
The data and heat map reveal notable differences in the device identification based on the meter and appliance type. The TV generally shows a high F1-score, especially with oZm meters, indicating recognizable consumption patterns. However, some OMPM measurements show lower scores due to sampling frequencies or specific operation modes.  
The oZm v3 shows a low F1-score for incandescent lamps, while the OMPM performs well in some cases. The simple on/off pattern of lamps may make it harder to differentiate. The vacuum cleaner performs well with the oZm v1, achieving a high F1-score, but the oZm v3 shows lower scores. The fan identification is generally good, but the OMPM yields a lower F1-score. The freezer performance is variable, with better results from the oZm v1. These findings emphasize the importance of selecting appropriate meters and algorithms for an optimal NILM performance.  
Although the heat map gives us a complete overview of all the measurements, it also clarifies the importance of combining all the measurements obtained from the appliances by the meter/dataset, as shown in Figure 10.  
Figure 9: The heat map for the F1-score.  
The oZm v1 shows the best overall performance in the F1-score, excelling in the detection of the vacuum cleaner (0.82), freezer (0.58), TV (0.71), and fan (0.67). The oZm v3, with more variables and a generally lower performance, has its best result on the TV (0.61), but reduced values on other devices, possibly due to the larger amount of equipment in its dataset. The OMPM shows a mixed performance, standing out in the incandescent lamp (0.68) and with moderate values in the fan (0.59), but with low scores in the television (0.35).