The change of power associated with technical losses \(dP_{w}\) in the section of the distribution network or installation wiring between sum meter and consumer MUT, can be calculated--provided the impedances of each segment and the respective current readings are known--using the expression  
\[dP_{w}=\sum_{i=1}^{N}I_{wi1}^{2}R_{wi}-\sum_{i=1}^{N}I_{wi1}^{2}R_{wi}, \tag{3}\]  
where \(I_{wi1}\) and \(I_{wi2}\) are the currents in the \(ith\) line (cable) segment, before and after the power step \(dP_{c}\), respectively, \(R_{wi}\) is wire resistance of the \(ith\) line's (cable's) segment, \(N\) is number of line segments between consecutive load connection points in the grid. Numerous methods have been proposed for estimate technical losses, which depend on available information, network topology, and voltage levels. For low-voltage distribution networks, accurate loss estimation becomes particularly challenging due to the large number of elements and insufficient operational data [38]. One of the most straightforward approaches to estimating losses in low-voltage grids involves comparing the total energy supplied and consumed [39; 40; 41; 42]. However, this method cannot distinguish between technical and non-technical losses [40; 43], making it unsuitable for anomaly detection. Moreover, it requires synchronized or time-aggregated readings from both the entry-point meter and all load meters downstream. In real-world settings, data from some consumers is often unavailable due to non-telemetered loads [44; 45]. Sometimes data from users is also unavailable due to disaster. Then, a load restoration method is required. This method must also consider losses (disconnections). In [46], an asynchronous decentralized load restoration method of the resilient electricity-transportation network considering building and electric bus was presented. Several methods have been developed to estimate missing data from non-telemetered customers or uncertain smart meter readings. For instance, [PERSON] and others [44] propose a top-down approach using intra-hour demand profiles generated via a Markov process, while [PERSON] and others [47] suggest a Dirichlet-sampled Gaussian mixture model. However, such synthetic profiles are insufficient to accurately estimate instantaneous power loss changes triggered by specific load events, which is critical for our method. Another class of techniques estimates technical losses via analytical approximations using parameters such as load form factor, loss factor, energy loss ratios, and empirically derived coefficients [38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55]. While these models are computationally simple and only require loading data at the entry point, their estimates are based on peak demand and general load profiles, lacking real-time responsiveness [48]. Thus, they are valuable for planning and optimization [40; 56; 57] but inadequate for event-based loss estimation in consumer delivery branches. Benchmarking methods approximate a target network by matching it to a representative circuit from a library of benchmark models [40; 45; 58]. Selection is based on similarity between various features such as feeder length, number of branches, and customer density [45; 59]. These techniques employ machine learning or statistical clustering to build and navigate the benchmark library [48]. However, the selected benchmark is an approximation rather than an exact match, which limits its usefulness for precise loss estimation [39]. Power flow analysis is a widely accepted method for estimating power losses [39; 60; 61; 62; 63; 60]. It can yield high precision [60] but requires complete knowledge of network parameters and all load data. Since our method relies only on readings from the sum meter and the consumer MUT and assumes no access to detailed grid topology or wiring specifications, neither power flow analysis nor analytical loss estimation via (3) is applicable for our purposes.