In [64], a fundamentally different technique is proposed based on [PERSON]'s law of cooling, which utilizes non-electrical parameters such as conductor and ambient temperatures. Although rooted in heat transfer physics, this method requires additional instrumentation (e.g., temperature sensors and data transmission systems) and suffers from slow thermal response and low sensitivity to small power changes, making it incompatible with our anomaly detection objectives. The PCC component associated with changes in grid loads \(dP_{nL}\), caused by a consumer load event \(\mathrm{d}P_{c}\), arises due to voltage fluctuations across the network. These voltage changes stem from altered current flows and the corresponding shifts in voltage drops. The response of each load to voltage variation--and hence its individual PCC component, \(dP_{i}\)--depends on the electrical nature of the load (e.g., constant power, constant current, or constant impedance), or their ZIP model composition [62; 65]. If the ZIP model parameters of all loads and their voltage readings are known, then \(\mathrm{d}P_{nL}\) can be calculated analytically:  
\[\mathrm{d}P_{nL}=\sum_{i=1}^{M}P_{IN}\Bigg{(}a_{i}+b_{i}\frac{V_{i2}}{V_{N}}+c _{i}\bigg{(}\frac{V_{i2}}{V_{N}}\bigg{)}^{2}\Bigg{)}-\sum_{i=1}^{M}P_{IN} \Bigg{(}a_{i}+b_{i}\frac{V_{i1}}{V_{N}}+c_{i}\bigg{(}\frac{V_{i1}}{V_{N}} \bigg{)}^{2}\Bigg{)}, \tag{4}\]  
where \(P_{IN}\) is power consumed by _ith_ load when its voltage equals to the nominal \(V_{i}=V_{N}\), \(a_{i},\ b_{i},\ c_{i}\) are the coefficients representing the constant power, constant current, and constant impedance components of the ZIP load model, \(V_{i2}\) and \(V_{i1}\) are actual voltages of the nodes where _ith_ load is connected, correspondingly, before power step \(\mathrm{d}P_{c}\) and after it. However, in practical deployments, voltage readings are often unavailable due to partial smart meter coverage, and the ZIP model parameters of connected loads are generally unknown. This significantly limits the applicability of (4) to real-world grid monitoring. For the identification of the anomaly \(\mathrm{d}P_{A}\
eq 0\), the suggested method requires evaluation of the PCC component \(\mathrm{d}P_{n}\) (1). As discussed, it could not be done by applying previously presented analytical approaches (3) and (4) as the method relies just on data provided by sum meter and consumer smart meter, and the currents, voltages and powers of other consumers' loads connected to the distribution branch monitored by the sum meter (except the one monitored by consumer MUT) are unknown. It also cannot be obtained directly from the data acquired by sum meter and MUT if PCC component associated with the anomaly \(\mathrm{d}P_{A}\) is suspected to be non-zero (1). However, \(\mathrm{d}P_{n}\) could be predicted by applying statistical analysis or machine learning (ML) methods. Therefore, the aim of this work is to propose and verify a method for the prediction of PCC in distribution branch, which is caused by the PCC at consumer site using readings (powers, voltages, currents) acquired by sum meter and meter under test. It is assumed that readings of the other consumer loads, connected to the distribution branch, are unavailable because of partial smart metering deployment. If consumer MUT readings are used as input predictors (variables) for input of the prediction model, then its systematic error will influence the target output errors. Therefore, PCC and consumer MUT errors cannot be evaluated separately and become a common consumer MUT error and PCC assessment. The main contribution of the paper is a method for estimation of PCC in the distribution branch in response to consumer load change. As can be seen from the survey of related literature (Table 1) of loss assessment techniques any method suitable for this specific task was not published earlier.