## 1 Introduction  
Energy consumption significantly impacts the economy and the environment, emphasizing the need for accurate measurement and optimization. Real-time monitoring enables cost reductions and enhances sustainability by promoting energy efficiency practices. Non-Intrusive Load Monitoring (NILM) is a promising approach to disaggregate electricity consumption without requiring additional sensors at each consumption point. Unlike intrusive methods, NILM estimates the individual device consumption by analyzing smart meter data [1]. Studies have shown that detailed information on appliance energy usage can reduce household consumption by at least 20% [2]. The field of NILM has significantly evolved since its initial conceptualization. Research on NILM began in the 1980s and 1990s, focusing on developing non-intrusive methods to monitor and disaggregate household energy consumption. In 1992, [PERSON] demonstrated how to estimate the individual appliance energy consumption from a single aggregated signal, using signal processing techniques and basic energy consumption models. Thiswork played an essential role in the development of the more advanced techniques used in NILM today [3]. NILM research has evolved significantly, incorporating sophisticated techniques to improve accuracy. Methods like Combinatorial Optimization (CO) and Hidden Markov Models (HMMs) were initially used. More recently, deep learning approaches, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have enhanced the NILM performance. However, challenges persist due to the lack of standardized datasets and evaluation metrics, complicating algorithm comparisons and limiting reproducibility. Additionally, the influence of hardware characteristics, particularly harmonics, on the NILM performance remains underanalyzed. As shown in Figure 1, the evolution of NILM has followed key technological advancements. In the early 2000s, more advanced algorithms incorporating load modeling and signal decomposition techniques emerged. These approaches explored energy signal decomposition into individual components and appliance identification based on distinctive load characteristics. With the rise of machine learning and advanced data analytics since the mid-2000s, NILM techniques have significantly evolved. Traditional approaches, such as decision trees, HMMs [4], and Factorial Hidden Markov Models (FHMMs) [5], enabled the capture of temporal dependencies in energy consumption data. Deep learning techniques have revolutionized NILM recently, significantly improving the disaggregation accuracy. CNNs and RNNs are increasingly used because of their ability to model complex, nonlinear relationships in energy data [6; 7; 8]. Recent comparative studies also analyze the performance of several machine learning techniques, including LSTM-based Recurrent Neural Networks [9] for load disaggregation in residential electricity consumption data [8]. These algorithms can automatically extract meaningful features from raw consumption data, enhancing the classification performance. Additionally, novel methods such as the Coupled Sequence Matrix Reconstruction and Cross-Stage Partial Network (CSPNet) have been proposed to improve the NILM performance by effectively leveraging variations in energy consumption over time [10]. Despite these advancements, NILM faces challenges, particularly regarding real-time processing capabilities [11]. Most NILM algorithms are not designed for real-time operations, limiting their practical applicability in scenarios requiring immediate responses. Figure 1: History and evolution of NILM (Source: own elaboration). While high-frequency hardware and advanced machine learning techniques can enhance the real-time performance, challenges such as the computational efficiency optimization and handling large volumes of high-frequency data persist [12]. Edge computing-based NILM solutions have gained attention for addressing these challenges.