## 4 Results and Discussion
### Metrics Obtained from the Different Datasets Generated with Open Hardware
#### 4.1.1 DSUALM and DSUALMH  
The DSUALMH (UAL dataset with harmonics) is a new high-resolution dataset specifically designed for NILM. It was published in July 2023, built upon the DSUALM dataset released in February 2022. This dataset was generated using six oZm devices [28] (Figure 6), which are open-source and open hardware energy meters capable of measuring electrical variables at a high sampling frequency of 15.625 Hz. The DSUALMH is a multichannel dataset that includes measurements from five specific household appliances (a fan, a freezer, a television, a vacuum cleaner, and a boiler) along with aggregated electricity consumption data. Its comprehensive structure features more than 150 electrical variables, encompassing complex harmonic values of the current, voltage, and power up to the 50 th order, as well as transient information.  
The creation and labeling process for DSUALMH involved collecting measurements via the OZM API, preprocessing the data to resolve date and time discrepancies, and converting the files into a CSV format. Subsequently, a new converter specifically developed and integrated into the NILMTK (ualm5t.convert_ualmt) was utilized to organize these files into a unified HDF5 format, complemented by the YAML metadata. This converter supports 13-digit timestamps. The DSUALMH dataset is publicly accessible to the research community and is hosted on the Zenodo platform [33], ensuring its long-term availability and promoting transparency and reproducibility in NILM research. Since its introduction in July 2023, the access to the dataset is already enabled through the University of Almeria's datasets on Zenodo.  
The dataset of UAL with harmonics (DSUALMH) is an extension of DSUALM, also obtained using the oZm v1 in a laboratory setting [34]. This dataset includes fundamental and secondary electrical characteristics, such as the complex harmonic current, voltage, and power values up to the 50 th order for the same five devices and aggregate data.  
The best results for nine algorithms are shown in Table 2. These findings suggest that harmonic support is more effective in statistical and traditional models in improving the accuracy and disaggregation. In contrast, in complex deep learning models, its impact is less deterministic and depends more on the architecture and the quality of the data. The choice of metrics must consider the type of problem (regression or classification) and the nature of the data to properly assess the improvement produced by harmonics.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{1}{c}{**MAE (W)**} & \multicolumn{1}{c}{**RMSE (W)**} & \multicolumn{1}{c}{**F1-Score**} & \multicolumn{1}{c}{**NDE**} & \multicolumn{1}{c}{**Runtime (s)**} \\ \hline
**Harmonics** & **No** & **Yes** & **No** & **Yes** & **No** & **Yes** & **No** & **Yes** & **No** & **Yes** \\ \hline CO & 457.39 & 455.3 & 511.08 & 510.48 & 0.57 & 0.56 & 108.38 & 107.95 & 1.22 & 1.33 \\ Mean & 387.05 & 241.29 & 389.42 & 242  
These datasets underscore the importance of incorporating harmonics and sampling strategies in NILM applications, as these factors can significantly enhance the accuracy of load disaggregation models.