Quantification of the impact of metering errors could be performed to determine economic losses due to incorrect metering. Some considerations on this issue were presented in our previous articles [13]. Typically, if meters are compliant with the corresponding standards, the impact of systematic errors (within allowed limits) and economic losses to consumers and energy providers are expected to be equally distributed, and no actions are applied to correct meters reported consumption readings. Many of the suggested methods are based on the analysis of long-term time series of power consumption and other electrical quantity data [29; 30; 31; 32; 33]. This strategy involves tracking energy usage patterns over time and comparing them to expected baseline behavior [29; 34]. Another widely applied strategy is based on power balance analysis, which requires aggregating measurements from a sum (total) meter and comparing them with the cumulative readings of all downstream submeters in the power distribution network [20; 21; 35; 36]. A sum meter is a smart meter that is installed at the beginning of the energy distribution branch and monitors total consumption of loads connected to the same branch and losses in the distribution electrical cables. The reliability and applicability of such methods diminish in practical deployment scenarios where smart metering coverage is incomplete or partially implemented. In our previous work [37], we proposed an alternative method for detection of anomalies in smart metering data, which is based on the observation of discrete power events, sudden changes in load resulting from consumer activity, such as switching appliances on or off. This approach involves the joint analysis of power, current and voltage measurements obtained simultaneously from a sum meter and a meter under test (MUT), both before and after such power events. The sum meter serves as a reference instrument which readings are trusted both at the period of training of models necessary for the implementation and monitoring period. A power event (or power step), is defined as a distinct transition in energy demand at a specific time. This direct comparative method between meters eliminates the need for exhaustive submetering of all individual loads beneath the sum meter, a requirement that constrains many conventional techniques. Our method is based on evaluating the active power balance associated with a power consumption change (PCC) during a power step. The key equation is expressed as follows:  
\[dP_{s}=dP_{c}+dP_{n}+dP_{A}, \tag{1}\]  
where \(dP_{s}\) is an active PCC at the sum meter installation point, \(dP_{c}\) is active PCC of the load located under the consumer MUT, \(dP_{A}\) is PCC associated with a presence of the anomaly and \(dP_{n}\) is the change of active power consumption in the distribution branch. As presented in (2), PCC in the distribution branch in response to the consumer load consumption change (power step) is a superposition of (1) change of energy transfer losses in the distribution branch between sum meter and consumer meter, and (2) change of power consumption by other loads connected to the same distribution branch. \[dP_{n}=dP_{w}+dP_{nL}. \tag{2}\]  
The reason for the \(dP_{w}\) is power losses in energy delivery cables, and the reason for the \(dP_{nL}\) is power consumption change due to voltage change caused by the change of the consumer load behind MUT. This decomposition facilitates building a prediction model as derived later, but its constituent parts \(dP_{w}\) and \(dP_{nL}\) are not measured separately in a real distribution network.