Estimation of Working Error of Electricity Meter Using Artificial Neural Network (ANN)

Murat Tasci and Hidir Duzkaya ()
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Murat Tasci: The Ministry of Industry and Technology, Directorate of General for Metrology and Industrial Product Safety, 06530 Ankara, Türkiye
Hidir Duzkaya: Department of Electrical-Electronics Engineering, Faculty of Engineering, Gazi University, 06570 Ankara, Türkiye

Energies, 2025, vol. 18, issue 5, 1-16

Abstract: Together with the rapidly growing world population and increasing usage of electrical equipment, the demand for electrical energy has continuously increased the demand for electrical energy. For this reason, especially considering the increasing inflation rates around the world, using an electricity energy meter, which works with the least operating error, has great economic importance. In this study, an artificial neural network (ANN)-based prediction methodology is presented to estimate an active electricity meter’s combined maximum error rate by using variable factors such as current, voltage, temperature, and power factor that affect the maximum permissible error. The estimation results obtained with the developed ANN model are evaluated statistically, and then the suitability and accuracy of the presented approach are tested. At the end of this research, it is understood that the obtained results can be used by high accuracy rate to estimate the combined maximum working error of an active electricity energy meter with the help of a suitable ANN model based on the internal variable factors.

Keywords: artificial neural network; combined maximum working error; electricity meter; metrology (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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