IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i5p1265-d1605564.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Murat Tasci & Hidir Duzkaya, 2025. "Estimation of Working Error of Electricity Meter Using Artificial Neural Network (ANN)," Energies, MDPI, vol. 18(5), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1265-:d:1605564
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/5/1265/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/5/1265/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ayushi Chahal & Preeti Gulia & Nasib Singh Gill & Jyotir Moy Chatterjee & Muhammad Ahmad, 2022. "Performance Analysis of an Optimized ANN Model to Predict the Stability of Smart Grid," Complexity, Hindawi, vol. 2022, pages 1-13, August.
    2. Marian Kampik & Artur Skórkowski & Michał Pecyna & Konrad Sowula, 2024. "The Influence of Subsequent Harmonics of the Load Current on Errors of Electronic Energy Meters," Energies, MDPI, vol. 17(5), pages 1-16, February.
    3. Carstens, Herman & Xia, Xiaohua & Yadavalli, Sarma, 2018. "Measurement uncertainty in energy monitoring: Present state of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2791-2805.
    4. Afzal, Sadegh & Ziapour, Behrooz M. & Shokri, Afshar & Shakibi, Hamid & Sobhani, Behnam, 2023. "Building energy consumption prediction using multilayer perceptron neural network-assisted models; comparison of different optimization algorithms," Energy, Elsevier, vol. 282(C).
    5. Rokonuzzaman, Md. & Rahman, Saifur & Hannan, M.A. & Mishu, Mahmuda Khatun & Tan, Wen-Shan & Rahman, Kazi Sajedur & Pasupuleti, Jagadeesh & Amin, Nowshad, 2025. "Levenberg-Marquardt algorithm-based solar PV energy integrated internet of home energy management system," Applied Energy, Elsevier, vol. 378(PA).
    6. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    7. Akshit Kurani & Pavan Doshi & Aarya Vakharia & Manan Shah, 2023. "A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting," Annals of Data Science, Springer, vol. 10(1), pages 183-208, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Thiago Conte & Roberto Oliveira, 2024. "Comparative Analysis between Intelligent Machine Committees and Hybrid Deep Learning with Genetic Algorithms in Energy Sector Forecasting: A Case Study on Electricity Price and Wind Speed in the Brazi," Energies, MDPI, vol. 17(4), pages 1-31, February.
    2. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    3. Xuecheng He & Jujie Wang, 2024. "A Hybrid Forecasting System Based on Comprehensive Feature Selection and Intelligent Optimization for Stock Price Index Forecasting," Mathematics, MDPI, vol. 12(23), pages 1-27, November.
    4. Fan Yang & Qian Mao, 2023. "Auto-Evaluation Model for the Prediction of Building Energy Consumption That Combines Modified Kalman Filtering and Long Short-Term Memory," Sustainability, MDPI, vol. 15(22), pages 1-16, November.
    5. Helmo K. Morales Paredes & Matheus Branco Arcadepani & Alexandre Candido Moreira & Flávio A. Serrão Gonçalves & Fernando Pinhabel Marafão, 2023. "Enlightening Load Modeling by Means of Power Factor Decompositions," Energies, MDPI, vol. 16(10), pages 1-22, May.
    6. Zhang, Weipan & Wu, Xianhua & Chen, Jihong, 2024. "Low-carbon efficiency analysis of rail-water multimodal transport based on cross efficiency network DEA approach," Energy, Elsevier, vol. 305(C).
    7. Li, Lei & Huang, Haihong & Zou, Xiang & Zhao, Fu & Li, Guishan & Liu, Zhifeng, 2021. "An energy-efficient service-oriented energy supplying system and control for multi-machine in the production line," Applied Energy, Elsevier, vol. 286(C).
    8. Chin Soon Ku & Jiale Xiong & Yen-Lin Chen & Shing Dhee Cheah & Hoong Cheng Soong & Lip Yee Por, 2023. "Improving Stock Market Predictions: An Equity Forecasting Scanner Using Long Short-Term Memory Method with Dynamic Indicators for Malaysia Stock Market," Mathematics, MDPI, vol. 11(11), pages 1-20, May.
    9. Qin, Fuli & Tong, Mingyu & Huang, Ying & Zhang, Yubo, 2024. "Modeling, prediction and analysis of natural gas consumption in China using a novel dynamic nonlinear multivariable grey delay model," Energy, Elsevier, vol. 305(C).
    10. Syed Hasan Jafar & Shakeb Akhtar & Hani El-Chaarani & Parvez Alam Khan & Ruaa Binsaddig, 2023. "Forecasting of NIFTY 50 Index Price by Using Backward Elimination with an LSTM Model," JRFM, MDPI, vol. 16(10), pages 1-23, September.
    11. Jiahao Chen & Xiaofei Li & Junjie Du, 2025. "Analysis of Frequent Trading Effects of Various Machine Learning Models," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1707-1740, March.
    12. Lucia Cattani & Anna Magrini & Valentina Leoni, 2022. "Energy Performance of Water Generators from Gaseous Mixtures by Condensation: Climatic Datasets Choice," Energies, MDPI, vol. 15(20), pages 1-24, October.
    13. Jin, Ting & Liang, Feiyan & Dong, Xiaoqi & Cao, Xiaojuan, 2023. "Research on land resource management integrated with support vector machine —Based on the perspective of green innovation," Resources Policy, Elsevier, vol. 86(PB).
    14. Fan, Yuling & Xia, Xiaohua, 2018. "Building retrofit optimization models using notch test data considering energy performance certificate compliance," Applied Energy, Elsevier, vol. 228(C), pages 2140-2152.
    15. Sun, Yang & Tian, Zhirui, 2025. "Solving few-shot problem in wind speed prediction: A novel transfer strategy based on decomposition and learning ensemble," Applied Energy, Elsevier, vol. 377(PD).
    16. Eren, Yavuz & Küçükdemiral, İbrahim, 2024. "A comprehensive review on deep learning approaches for short-term load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    17. Mokhtar Jlidi & Oscar Barambones & Faiçal Hamidi & Mohamed Aoun, 2024. "ANN for Temperature and Irradiation Prediction and Maximum Power Point Tracking Using MRP-SMC," Energies, MDPI, vol. 17(12), pages 1-21, June.
    18. Farwah Ali Syed & Kwo-Ting Fang & Adiqa Kausar Kiani & Muhammad Shoaib & Muhammad Asif Zahoor Raja, 2025. "Design of Neuro-Stochastic Bayesian Networks for Nonlinear Chaotic Differential Systems in Financial Mathematics," Computational Economics, Springer;Society for Computational Economics, vol. 65(1), pages 241-270, January.
    19. Manuel Jaramillo & Wilson Pavón & Lisbeth Jaramillo, 2024. "Adaptive Forecasting in Energy Consumption: A Bibliometric Analysis and Review," Data, MDPI, vol. 9(1), pages 1-23, January.
    20. Sulman Shahzad & Elżbieta Jasińska, 2024. "Renewable Revolution: A Review of Strategic Flexibility in Future Power Systems," Sustainability, MDPI, vol. 16(13), pages 1-24, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1265-:d:1605564. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.