## 4 Results and Discussion
### Metrics Obtained from the Different Datasets Generated with Open Hardware
#### 4.1.3. DSUALM10H and DSUALM10  
In June 2023, the dataset of the UAL for 10 appliances with harmonics (DSUALM10H) was expanded to include 10 additional appliances using three oZm v3 devices (see Figure 8), significantly improving its scope and diversity. Figure 8: Schema of appliances used in DSUALM10H (Source: own elaboration). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **MAE (W)** & **RMSE (W)** & **F1-Score** & **NDE** & **Runtime (s)** \\ \hline CO & 9.49 & 12.26 & 0.7 & 0.43 & 1.49 \\ Mean & 13.19 & 12.88 & 0.17 & 0.48 & 0.58 \\ FHMM & 12.71 & 15.53 & 0.66 & 0.5 & 1.7 \\ DAE & \(4.01\times 10^{12}\) & \(2.10\times 10^{13}\) & 0.2 & 3.54 \(\times\) 10\({}^{10}\) & 27.07 \\ RNN & 13.36 & 14.53 & 0.17 & 0.51 & 2723.48 \\ Seq2 Point & 11.6 & 12.65 & 0.17 & 0.56 & 841.65 \\ Seq2 Seq & \(6.04\times 10^{11}\) & \(1.52\times 10^{13}\) & 0.2 & 9.06 \(\times\) 10\({}^{10}\) & 34.67 \\ WindowGRU & 8.73 & 10.55 & 0.51 & 0.43 & 3869.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: UALM2 metrics with eight algorithms, one-second sampling, and no filters. The DSUALM10H dataset is a high-resolution, multichannel dataset for NILM, released in June 2023. It was created using three open-source oZm v3 devices, capturing twelve channels--including the aggregated main consumption and ten common household appliances--with a sampling rate of 15.625 Hz. The dataset comprises 150 electrical variables, including the current, voltage, and power harmonics up to the 50 th order, as well as transient information, allowing for a detailed analysis of both fundamental and harmonic electrical characteristics. The data acquisition and preprocessing involved converting raw measurements to CSV and then to a unified HDF5 format with YAML metadata via a dedicated NILMTK converter (ualmt10h.convert_ualmt10h), ensuring accurate storage and facilitating reproducibility. Publicly available [36], DSUALM10H promotes transparency and supports advanced NILM research by providing rich, open-access data generated with open hardware [37]. DSUALM10, by contrast, is a new dataset derived from DSUALM10H with harmonic components removed. This allows researchers to compare the impact of harmonics on the NILM performance. Table 4 presents the results obtained using nine algorithms with one-second sampling, without any filter, and with and without harmonics. The comparison highlights the role of harmonics in improving certain metrics for specific algorithms. The analysis suggests that, on average, the inclusion of harmonics does not significantly improve the overall performance of NILM algorithms across all appliances. While some algorithms show moderate improvements in metrics such as the NDE, F1-score, RMSE, and MAE when harmonics are considered, others show little to no benefit or even a slight decrease in performance. This indicates that the effectiveness of harmonics depends on the specific algorithm and its modeling approach rather than universally enhancing results. Furthermore, the impact of harmonics on the runtime varies by algorithm; in some cases, harmonics reduce the execution time, while in others, they significantly increase it. This highlights a trade-off between accuracy and computational cost that should be carefully considered when choosing to incorporate harmonics in NILM methods. DSUALM10 and DSUALM10H provide valuable and diverse data for testing and validating these algorithms under various measurement conditions. They provide essential insights into how the inclusion of harmonics and different analytical approaches affects