## 6 Conclusions  
A method has been developed to predict the power consumption change (PCC) in a distribution grid branch resulting from consumer load switching events. The predicted PCC enhances the accuracy of the active power balance equation, which is applied to the power readings from both the sum meter and the consumer smart meters at the moment of the consumer power demand change. By leveraging this improved power balance, the gain error in active power (or in voltage and current) measurements can be estimated, and other smart metering anomalies can be detected.  
The total PCC in the distribution grid is decomposed into two components: one associated with distribution line losses and the other with consumers' load variations. The first component is approximated using an analytical expression based on an equivalent resistance value, estimated under reference conditions (i.e., during the training phase). The second component is approximated through a data-driven approach, which uses readings collected during these reference conditions from sum meter and consumer meter under test.  
The accuracy of power gain error and PCC estimations is primarily influenced by the number of power events (and corresponding electrical measurements) used for model derivation and for the assessment of target values during the monitoring (testing) period. The improvement of accuracy class of the consumer meter from typical Class 1 to Class 0.5 was not found to have a noticeable influence on PCC prediction error, due to the statistical averaging of a sufficiently large number of events and other factors influencing accuracy of minimization problem solution necessary to obtain PCC estimate. Based on a synthesized dataset, the absolute error in estimating the power measurement gain error was within \(\pm\)0.3%, and the relative error in estimating the PCC in the distribution branch was within \(\pm\)10%. Both levels of precision are considered acceptable for practical applications, given that the objective of the methodology is not to replace formal metrological verification or laboratory calibration, but rather to enable anomaly detection in smart metering, which can trigger further investigation via regulated metrological procedures or consumer complaint handling mechanisms.  
An experimental validation of the methodology was conducted using a laboratory testbed equipped with two commercial smart meters, confirming the suitability of the methodology for estimating measurement errors and assessing changes in power consumption in the grid segment between the sum and consumer meter. However, field tests in an actual distribution grid environment are still required to further validate the methodology under real-world operating conditions.  
**Author Contributions:** Conceptualization, M.S., E.V. and Z.N.; methodology, Z.N.; software, M.S. and J.S.; validation, M.S., J.S., K.Z. and V.D.; investigation, E.V.; data curation, R.L. and V.D.; writing--original draft preparation, R.L., M.S. and J.S.; writing--review and editing, Z.N.; visualization, M.S. and J.S.; funding acquisition, Z.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Research Council of Lithuania (LMTLT), agreement No S-MIP-24-42.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original data presented in the study are openly available at [[https://github.com/KTU-ANODETEL/Grid_of_6_Nodes_One_Phase_Dataset1](https://github.com/KTU-ANODETEL/Grid_of_6_Nodes_One_Phase_Dataset1)]([https://github.com/KTU-ANODETEL/Grid_of_6_Nodes_One_Phase_Dataset1](https://github.com/KTU-ANODETEL/Grid_of_6_Nodes_One_Phase_Dataset1)), accessed on 14 June 2025.  
**Conflicts of Interest:** The authors declare no conflicts of interest.