## 2 NILmtk  
NILM relies on advanced software tools and specialized hardware to ensure accurate data acquisition and reliable energy consumption disaggregation. In addition, data acquisition systems have evolved with high-resolution hardware, improving NILM's accuracy across various environments. These advancements drive its adoption in industrial and commercial applications, contributing to energy savings and a reduced environmental footprint. Future developments in NILM are expected to bring further innovations, enhancing its precision and applicability. In the last decade, efforts to standardize the evaluation of NILM algorithms and expand the access to public datasets have intensified. Platforms like NILM-Eval and the NILMTK have significantly facilitated the comparison and validation of load disaggregation approaches. Among the software tools, the NILMTK stands out as a standardized framework that provides workflows and analytical algorithms, facilitating research and methodology comparisons. Its application in energy efficiency studies has proven valuable for optimizing the electricity consumption in residential buildings [21]. The NILMTK [22] is an open-source toolkit designed to streamline NILM research by providing standardized data formats, detailed metadata, and a variety of energy analysis algorithms [23]. Its modular architecture supports the entire workflow, from the data acquisition to the performance evaluation. It is compatible with datasets such as REDD [24], UK-DALE [25], ECO [26], and other high-resolution datasets, such as those provided by Pecan Street, (Austin, TX, USA), through its dataport platform [27], which provides tools for preprocessing, feature extraction, and visualization and enables the comparison of results using built-in evaluation metrics. As shown in Figure 2, NILMTK covers all key steps, including the raw data processing, the application of learning algorithms, and the disaggregation performance assessment. Among the disaggregation methods implemented in the NILMTK, CO and the FHMM are two widely used benchmark approaches. CO formulates the load disaggregation as an optimization problem, where the total energy consumption is distributed among appliances while minimizing the error between the measured and estimated consumption. Despite its computational complexity, CO has proven to be both efficient and competitive compared to more sophisticated methods, while also being significantly faster [28]. The FHMM [29] extends the traditional HMM by modeling multiple appliances independently, improving the representation of the appliance behavior, and capturing more detailed consumption patterns. In the NILMTK, the FHMM is integrated within its modular framework, facilitating its application across various datasets. To evaluate NILM algorithms, the NILMTK incorporates several performance metrics that measure the accuracy of the load disaggregation. Among these, the \(F1\)-_score_ defined in Equation (1) assesses the balance between precision and recall in identifying appliance states. \[F1=\frac{2\times Precision\times Recall}{Precision+Recall}, \tag{1}\]  
where _Precision_, defined in Equation (2), emphasizes the accuracy of detecting the \"ON\" state of an appliance, and _Recall_, described in Equation (3), focuses on the correct iden  
Figure 2: Pipeline of NILMTK (Source: own elaboration). tification of real activations (_TP_ refers to true positives, _FP_ to false positives, and _FN_ to false negatives). \[Precision=\frac{TP}{TP+FP}, \tag{2}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{3}\]  
Another key metric is the Energy Assignment Error (_EAE_), defined in Equation (4), which measures the mean absolute error in the energy estimation. \[EAE=\left|\sum_{t}y_{t}^{(n)}-\ \sum_{t}\hat{y}_{t}^{(n)}\right|, \tag{4}\]  
where \(\hat{y}_{t}^{(n)}\) is the estimated power consumption of the appliance \(n\) at time interval \(t\), and \(y_{t}^{(n)}\) is the actual power used by the same appliance.