# The Impact of Power Definitions on the Disaggregation of Home Loads for Smart Meter Measurements
## 4 Results
### Evaluation  
The evaluation process considered three approaches described in the previous section: the classical approach using only active power, [PERSON]'s power definitions, and [PERSON]'s power definitions. Table 2 presents the appliance identification accuracy of the proposed methods for aggregated loads calculated using Equation (23). For instance, the first row of Table 2 shows the results for a combination of eleven active and three inactive appliances. For example, the 78.6% accuracy for Active Power with 14 total appliances indicates that the algorithm correctly identified 11 of the 14 appliance loads. It is important to note that multiple units of the same appliance type can be active within a combination. As shown in Table 2, both [PERSON]'s and [PERSON]'s power definitions achieved 100% accuracy for several combinations with varying numbers of active appliances. However, [PERSON]'s power definition consistently outperformed the others in the remaining scenarios. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Actual Turned-on Appliances**} & \multicolumn{3}{c}{\(A_{OAL}\)} \\ \cline{2-4}  & _Active Power_ & _[PERSON]’s_ & _[PERSON]’s_ \\ \hline
8 LED + 1 Flu + 1 VC + 1 WM + 1 DH + 1 Toa + 1 CM + 1 OCv + 1 AC + 2 Lap + 3 MP & _78.6\%_ & _100\%_ & _100\%_ \\ \hline
7 LED + 1 MO + 1 MP + 2 Lap + 1 Fri + 1 Toa + 1 OCv + 1 AC + 1 VC & _57.1\%_ & _71.4\%_ & _100\%_ \\ \hline
1 LED + 1 Flu + 1 VC + 1 WM + 1 DH + 1 Toa + 1 MO + 1 CM + 1 Ove + 1 AC + 2 Lap + 1 MP & _100\%_ & _100\%_ \\ \hline
8 LED + 1 MO + 2 Lap + 1 LCD + 1 Toa + 1 DH & _50\%_ & _100\%_ & _100\%_ \\ \hline
3 LED + 2 Flu + 1 DH + 1 Toa + 1 MO + 2 AC + 1 LCD + 1 LAP + 2 MP & _71.4\%_ & _100\%_ & _100\%_ \\ \hline
1 LED + 1 VC + 1 WM + 1 DH + 1 MO + 1 Ove + 2 AC + 2 LCD + 2 Lap & _50\%_ & _64.2\%_ & _78.5\%_ \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Appliance identification accuracy for the proposed methods. In addition, the results of the OMA of the appliance disaggregation for all the aggregated load combinations (determined by (24)) can be seen in Table 3. The best OMA was 59.8% for [PERSON]'s aggregated power, which means that in 100 combinations of aggregate appliances, more than 59 combinations were obtained in which all the appliances were correctly disaggregated. This index also showed that the algorithm with [PERSON]'s proposal is the one that presents the best results. In the case of the algorithm that only uses the active power, the obtained result clearly showed the weakness of this approach. These results emphasize the significance of considering various power definitions. The use of active power alone resulted in poor appliance disaggregation performance. In contrast, the more comprehensive power definition proposed by [PERSON] yielded the best results. It is important to note that all smart meters are subject to intrinsic errors, particularly in the presence of disruptive loads or power supply networks with high Total Harmonic Distortion (THD). This can impact experimental results. In this study, harmonics up to the 32 nd order were considered [60]. Data were collected from various locations to capture different THD levels. While some locations exhibited minimal distortion, most modern power networks are affected by distortions due to the prevalence of electronic and disruptive loads. The previous results were obtained for a grid voltage with a THD of 1.9%. To assess the impact of increased grid voltage distortion, additional tests were conducted with a THD of approximately 3%. Table 4 presents the appliance identification accuracy for this higher distortion level. As expected, increased voltage distortion negatively affected the performance of all approaches, reducing the accuracy of identifying active appliances. The active power-based approach failed to achieve 100% accuracy in any combination. In contrast, [PERSON]'s aggregated power approach consistently outperformed the others, achieving 100% accuracy in five combinations.