## 1 Introduction
By processing energy consumption data locally in real-time, edge-based NILM offers a lower latency, which is crucial for applications such as home automation and anomaly detection [13]. Moreover, local processing enhances privacy by minimizing data transmission, reducing the risk of data breaches, and decreasing bandwidth usage, making it more cost-effective. However, implementing NILM on the edge involves challenges, such as hardware limitations and energy consumption constraints, which must be addressed to fully realize its potential in energy management and smart home automation [14]. Table 1 provides an overview of various NILM methods, highlighting their strengths and limitations while showcasing their practical implementations in smart home energy management, industrial monitoring, and intelligent energy distribution systems. Practical applications of NILM are diverse and impactful. In smart home energy management, NILM identifies energy-saving opportunities and optimizes consumption, helping households reduce their energy bills and environmental footprint. In industrial monitoring, NILM facilitates the real-time monitoring and optimization of energy usage, allowing industries to streamline operations and improve efficiency. Lastly, NILM enhances the energy management efficiency in intelligent energy distribution systems by providing detailed insights into energy consumption patterns. This is crucial for optimizing smart grid applications and ensuring a more sustainable energy supply. NILM optimizes energy consumption, identifies usage patterns, and supports cost-saving decisions. This is achieved by disaggregating the total energy consumption into individual components, allowing more efficient demand management and identifying energy-saving opportunities [15]. Despite the advancements, some challenges persist: \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Advantages** & **Disadvantages** & **Practical Applications** \\ \hline Traditional models (HMM, FHMM, and decision trees) & Interpretable; requires less training data & Lower accuracy in scenarios with high consumption variability & Used in early NILM implementations, such as energy monitoring systems in research projects like REDD and UK-DALE datasets \\ \hline Neural networks (CNN, RNN, and deep learning) & High accuracy, can capture complex patterns & Require large amounts of data and high computational power & Applied in smart home energy management platforms like Google’s Nest and Sense Energy Monitor \\ \hline Advanced techniques (CSPNet and Coupled Sequence Matrix Reconstruction) & Improved performance by leveraging temporal variations & Still in experimental stages; lack of standardized metrics & Being tested in advanced NILM research for industrial and smart grid applications \\ \hline Edge computing-based & Lower latency, enhanced privacy, and reduced bandwidth usage & Hardware limitations and energy consumption constraints & Used in IoT-enabled smart meters to provide real-time feedback in smart homes and microgrid management \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key features of NILM methods. Standardized datasets: No accepted standard makes comparisons between studies difficult. The NILMTK aims to address this problem with the HDF5 format [16; 17];
* Diverse electrical loads: The devices' energy consumption variability complicates the NILM algorithm's practical development. This includes the complexity of identifying devices with similar or variable consumption patterns [17; 18];
* Robust and scalable solutions: Many algorithms perform well in laboratory settings but fail in real-world applications. This is due to the need to adapt to different conditions and usage patterns in the real world [19];
* Metrics for evaluation and generality: The inconsistency in performance metrics and the lack of standardized benchmarks make it challenging to evaluate the generality and effectiveness of new algorithms [17; 20]. This study addresses these challenges through several key strategies.