ILM connects directly to appliances to gather precise data, offering detailed insights at a higher cost and invasiveness. NILM, in contrast, assesses overall consumption from a single point, distinguishing individual appliances through algorithmic estimations. It is recognized for its cost-efficiency and minimally invasive setup [3]. NILM has surged ahead, favored for its non-disruptive nature, straightforward data collection, and low overheads. As the preferred method for electrical behavior analysis, NILM's various strategies have been comprehensively cataloged by [PERSON] and [PERSON] [4]. Current NILM-related research primarily focuses on identifying household electrical equipment due to NILM technology's unique characteristics. With each type of equipment exhibiting distinct electrical behavior characteristics during operation, numerous researchers are exploring NILM methods for device recognition based on these traits. The classification of household electrical equipment types or working modes is fundamental to this identification process. [PERSON] initially proposed a classification system for household electrical equipment, categorized as follows [5]:  
1. Resistive appliances: heating devices and similar equipment characterized by zero reactive power, no transient during activation, and the absence of harmonics. 2. Pump-operated appliances: motor-driven, with substantial reactive power. 3. Motor-driven appliances: examples include washing machines and fans, exhibiting less substantial activation transients. 4. Electronically fed appliances: characterized by short but high-amplitude activation transients. 5. Electronic power control appliances. 6. Fluorescent lighting. In the realm of NILM technology for identifying electrical equipment, diverse parameter signals are utilized as the basis for analysis. These include power signatures with consistent real and reactive power [6; 7; 8; 9; 10], voltage and current trajectories [11; 12], and harmonic analysis [13; 14; 15; 16]. Each of these studies significantly contributes to the performance of various analysis methods. Recent advancements in NILM have significantly contributed to the field of energy management, offering innovative solutions for disaggregating total energy consumption into appliance-level usage, focusing on adaptive modeling, machine learning algorithms, and integration with IoT for both residential and industrial applications. In the realm of adaptive modeling and machine learning for non-intrusive load monitoring (NILM), significant strides have been made by researchers like [PERSON] et al. [17] and [PERSON] et al. [18]. Their pioneering work in employing convolutional neural networks (CNNs) and adaptive models addresses the dynamic nature of electrical loads and grid conditions, a critical aspect in the evolution of NILM technologies. [PERSON] and [PERSON] [19] applied supervised graph signal processing for residential load disaggregation, emphasizing the importance of NILM in demand-side management. Focusing on the challenge of appliance identification and load disaggregation, studies by [PERSON] and [PERSON] [20], [PERSON] et al. [21], and [PERSON] et al. [22] underscored the critical role of accurate appliance recognition in enhancing the efficiency and reliability of NILM systems, utilizing time series analysis and deep feature-guided mechanisms. Addressing the practical challenges and opportunities in real-world NILM applications, [PERSON] et al. [23] provided an insightful overview, particularly emphasizing the potential of machine learning in overcoming these obstacles. In the integration of IoT and multimodal data into NILM, [PERSON] et al. [24] and [PERSON] et al. [25] showcased the potential of technology-enhanced systems in smart energy management, marking a significant advancement in the field. In industrial and complex environments, the works of [PERSON] et al. [26], [PERSON] et al. [27], and [PERSON] et al.