A Method for Assessment of Power Consumption Change in Distribution Grid Branch After Consumer Load Change
Source abstract
Abstract
This research targets prediction of power consumption change (PCC) in the branch of electrical distribution grid between a sum meter and consumer meter in response to consumer load change. The problem is relevant for power preservation law-based event-driven methods aiming for detection of anomalies like meter errors, electricity thefts, etc. The PCC in the branch is due to the change of technical (wiring) losses as well as change of power consumption of loads connected to the same distribution branch. Using synthesized dataset set a data-driven model is built to predict PCC in the branch. Model performance is assessed using root mean squared error (RMSE), mean absolute, and mean relative error, together with their standard deviations. The preliminary experimental verification using a test bed confirmed the potential of the method. The accuracy of the PCC in the branch prediction is influenced by the systematic error of the meters. Therefore, the error of the consumer meter and the PCC in the branch cannot be evaluated separately. It was observed that the absolute error of the estimate of power measurement gain error was observed to be within ±0.3% and the relative error of PCC in the branch prediction was within ±10%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Electricity Theft Detection Techniques · Smart Grid Energy Management · Energy Load and Power Forecasting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, LT-51368 Kaunas, Lithuania
Faculty of Informatics, Kaunas University of Technology, LT-51368 Kaunas, Lithuania
Funding
Research Council of Lithuania
References (70)
Show source-provided references
1. Data science for building energy management: A review10.1016/j.rser.2016.11.132 ↗
2. Rind, Y.M., Raza, M.H., Zubair, M., Mehmood, M.Q., and Massoud, Y. (2023). Smart Energy Meters for Smart Grids, an Internet of Things Perspective. Energies, 16.10.3390/en16041974 ↗
3. Behind the Meter Strategies: Energy management system with a Swedish case study10.1109/MELE.2021.3093638 ↗
4. An analysis of smart meter technologies for efficient energy management in households and organizations10.1016/j.egyr.2022.03.041 ↗
5. Koukouvinos, K.G., Koukouvinos, G.K., Chalkiadakis, P., Kaminaris, S.D., Orfanos, V.A., and Rimpas, D. (2025). Evaluating the Performance of Smart Meters: Insights into Energy Management, Dynamic Pricing and Consumer Behavior. Appl. Sci., 15.10.3390/app15020960 ↗
6. Energy meters evolution in smart grids: A review10.1016/j.jclepro.2019.01.229 ↗
7. A new IoT-based smart energy meter for smart grids10.1002/er.5177 ↗
8. A Comprehensive Review of Smart Energy Meters in Intelligent Energy Networks10.1109/JIOT.2015.2512325 ↗
9. Kilius, Š., Gailius, D., Knyva, M., Balčiūnas, G., Meškuotienė, A., Dobilienė, J., Joneliūnas, S., and Kuzas, P. (2024). Time Delay Characterization in Wireless Sensor Networks for Distributed Measurement Applications. J. Sens. Actuator Netw., 13.10.3390/jsan13030031 ↗
10. Smart Meters for Smart Energy: A Review of Business Intelligence Applications10.1109/ACCESS.2023.3326724 ↗
11. Scalable prediction-based online anomaly detection for smart meter data10.1016/j.is.2018.05.007 ↗
12. Detection of energy theft and defective smart meters in smart grids using linear regression10.1016/j.ijepes.2017.04.005 ↗
13. Nakutis, Ž., and Kaškonas, P. (2020). A Contemplation on Electricity Meters In-Service Surveillance Assisted by Remote Error Monitoring. Energies, 13.10.3390/en13205245 ↗
14. Sun, Y., Sun, X., Hu, T., and Zhu, L. (2023). Smart Grid Theft Detection Based on Hybrid Multi-Time Scale Neural Network. Appl. Sci., 13.10.3390/app13095710 ↗
15. Zhang, L., Wan, L., Xiao, Y., Li, S., and Zhu, C. (2019, January 21–23). Anomaly Detection method of Smart Meters data based on GMM-LDA clustering feature Learning and PSO Support Vector Machine. Proceedings of the 2019 IEEE Sustainable Power and Energy Conference (iSPEC), Beijing, China.10.1109/iSPEC48194.2019.8974989 ↗
16. Detection for Non-Technical Loss by Smart Energy Theft with Intermediate Monitor Meter in Smart Grid10.1109/ACCESS.2019.2940443 ↗
17. A Hierarchical Framework for Smart Grid Anomaly Detection Using Large-Scale Smart Meter Data10.1109/TSG.2017.2697440 ↗
18. Utomo, D., and Hsiung, P.-A. (2020). A Multitiered Solution for Anomaly Detection in Edge Computing for Smart Meters. Sensors, 20.10.3390/s20185159 ↗
19. Distributed Anomaly Detection in Smart Grids: A Federated Learning-Based Approach10.1109/ACCESS.2023.3237554 ↗
20. Smart Meter Error Estimation in Topological Low Voltage Energy System10.1109/ACCESS.2024.3471669 ↗
21. Kong, X., Ma, Y., Zhao, X., Li, Y., and Teng, Y. (2019). A Recursive Least Squares Method with Double-Parameter for Online Estimation of Electric Meter Errors. Energies, 12.10.3390/en12050805 ↗
22. Operational Status Evaluation of Smart Electricity Meters Using Gaussian Process Regression with Optimized-ARD Kernel10.1109/TII.2023.3274218 ↗
23. Liu, Y., Zhao, Q., Liang, D., Han, Y., Zhang, Y., and Guo, H. (2023, January 7–9). Research on Operation Error Detection Model of Metering Device. Proceedings of the 2023 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia), Chongqing, China.10.1109/ICPSAsia58343.2023.10294637 ↗
24. Helong, L., Haibo, Y., and Jinshuai, Y. (2019, January 13–16). Intelligent Energy Meter Fault Prediction Based on Machine Learning. Proceedings of the 2019 15th International Conference on Computational Intelligence and Security (CIS), Macao, China.10.1109/CIS.2019.00069 ↗
25. Evaluation of Operating State for Smart Electricity Meters Based on Transformer–Encoder–BiLSTM10.1109/TII.2022.3172182 ↗
26. Feng, Z., Yingying, C., Jie, D., Ling, F., Ji, X., Jiaming, Z., and Huayong, Z. (2019, January 24–26). Construction of Multidimensional Electric Energy Meter Abnormal Diagnosis Model Based on Decision Tree Group. Proceedings of the 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China.10.1109/ITAIC.2019.8785649 ↗
27. Analyzing adjustment and verification errors in electric metering devices for smart power systems considering multiple environmental factors10.1063/5.0208799 ↗
28. A framework for remote in-service metrological surveillance of energy meters10.1016/j.measurement.2020.108438 ↗
29. Combating electricity theft in smart grids: Accurate location identification using machine learning and the fast Walsh–Hadamard transform10.1007/s00202-024-02824-8 ↗
30. Bondok, A., Abdelsalam, O., Badr, M., Mahmoud, M., Alsabaan, M., Alsaqhan, M., and Ibrahem, M.I. (2024). Accurate Power Consumption Predictor and One-Class Electricity Theft Detector for Smart Grid “Change-and-Transmit” Advanced Metering Infrastructure. Appl. Sci., 14.10.3390/app14209308 ↗
31. Bonci, A., Fredianelli, L., Kermenov, R., Longarini, L., Longhi, S., Pompei, G., Prist, M., and Verdini, C. (2024). DeepESN Neural Networks for Industrial Predictive Maintenance through Anomaly Detection from Production Energy Data. Appl. Sci., 14.10.3390/app14198686 ↗
32. Oprea, S.-V., Bâra, A., Puican, F.C., and Radu, I.C. (2021). Anomaly Detection with Machine Learning Algorithms and Big Data in Electricity Consumption. Sustainability, 13.10.3390/su131910963 ↗
33. Pereira, J., and Silveira, M. (2018, January 17–20). Unsupervised Anomaly Detection in Energy Time Series Data Using Variational Recurrent Autoencoders with Attention. Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA.10.1109/ICMLA.2018.00207 ↗
34. Žarković, M., and Dobrić, G. (2024). Artificial Intelligence for Energy Theft Detection in Distribution Networks. Energies, 17.10.3390/en17071580 ↗
35. Korhonen, A. (2012). Verification of Energy Meters Using Automatic Meter Reading Data. [Master’s Thesis, School of Electrical Engineering, Aalto University].
36. A Data-Based Approach for Smart Meter Online Calibration10.21014/acta_imeko.v9i2.777 ↗
37. A Method for Noninvasive Remote Monitoring of Energy Meter Error Using Power Consumption Profile10.1109/TIM.2020.3002402 ↗
38. Strategy for analysis of loss situation and identification of loss sources in electricity distribution networks10.3176/oil.2007.2S.10 ↗
39. Using smart meters to estimate low-voltage losses10.1049/iet-gtd.2017.1300 ↗
40. Pilo, F., Bertani, A., Celli, G., Tardio, M., Soma, G.G., and Pannunzio, G. (2015, January 15–18). Technical Losses Assessment in Distribution Systems with Reduced Measurement Capabilities. Proceedings of the 23rd International Conference on Electricity Distribution, Lyon, France.
41. Lawanson, T., Ravikumar, K., Schulz, D., and Cecchi, V. (2019, January 6–9). Loss Estimation and Visualization in Distribution Systems using Smart Meter and Recloser Data. Proceedings of the 2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT and AI (HONET-ICT), Charlotte, NC, USA.10.1109/HONET.2019.8908067 ↗
42. Rendroyoko, I., Nungtjik, I., and Suhana, H.H. (2022, January 22–23). Development of Large Scale, End-to-End Online Monitoring Losses (OML). Proceedings of the 2022 5th International Conference on Power Engineering and Renewable Energy (ICPERE), Bandung, Indonesia.10.1109/ICPERE56870.2022.10037446 ↗
43. Three-Phase State Estimation for Distribution-Grid Analytics10.3390/cleantechnol3020022 ↗
44. Velasco, J.A., Amaris, H., Alonso, M., and Miguelez, M. (2018, January 4–7). Stochastic Technical Losses Analysis of Smart Grids under Uncertain Demand. Proceedings of the 2018 53rd International Universities Power Engineering Conference (UPEC), Glasgow, UK.10.1109/UPEC.2018.8542099 ↗
45. Velasco, J.A., Amaris, H., Alonso, M., and Casas, M. (2019, January 3–6). Energy losses estimation tool for Low Voltage Smart grids. Proceedings of the CIRED Conference-25th International Conference on Electricity Distribution, Madrid, Spain.
46. Li, Z., Sun, H., Xue, Y., Li, Z., Jin, X., and Wang, P. (IEEE Trans. Transp. Electrif., 2025). Resilience-Oriented Asynchronous Decentralized Restoration Considering Building and E-Bus Co-Response in Electricity-Transportation Networks, IEEE Trans. Transp. Electrif., early access.10.1109/TTE.2025.3581349 ↗
47. Dirichlet Sampled Capacity and Loss Estimation for LV Distribution Networks with Partial Observability10.1109/TPWRD.2020.3025125 ↗
48. Deep Learning loss model for large-scale low voltage smart grids10.1016/j.ijepes.2020.106054 ↗
49. Au, M.T., Anthony, T.M., Kamaruddin, N., Verayiah, R., Syed Mustaffa, S.A., and Yusoff, M. (2008, January 1–3). A simplified approach in estimating technical losses in distribution network based on load profile and feeder characteristics. Proceedings of the 2008 IEEE 2nd International Power and Energy Conference, Johor Bahru, Malaysia.
50. Energy Losses Estimation in Power Distribution Systems10.1109/TPWRS.2012.2188107 ↗
51. A Top-Down Approach for Distribution Loss Evaluation10.1109/TPWRD.2009.2014266 ↗
52. Improved lsf method for loss estimation and its application in dg allocation10.1049/iet-gtd.2015.1471 ↗
53. High-Quality Load Pattern Reconstruction from Smart Meter Data to Enhance the Assessment of Peak Power and Network Losses10.1109/TIA.2022.3159304 ↗
54. Evaluation of energy losses in low voltage distribution grids with high penetration of distributed generation10.1016/j.apenergy.2019.113907 ↗
55. Zhu, W., Zhou, Q., An, H., Chen, Z., Liu, S., and Yu, K. (2017, January 20–23). Analysis of the correlation between characteristic of equivalent load and power loss in smart distribution grid. Proceedings of the 2017 2nd International Conference on Power and Renewable Energy (ICPRE), Chengdu, China.10.1109/ICPRE.2017.8390591 ↗
56. Wang, S., Dong, P., and Tian, Y. (2017). A Novel Method of Statistical Line Loss Estimation for Distribution Feeders Based on Feeder Cluster and Modified XGBoost. Energies, 10.10.3390/en10122067 ↗
57. Wang, B., Sun, Y., Lu, F., and Wu, X. (2015, January 25–26). The Calculation and Analysis of Distribution Network Loss with Photovoltaic Power Generation Connected. Proceedings of the 3rd International Conference on Advances in Energy and Environmental Science 2015, Zhuhai, China.10.2991/icaees-15.2015.100 ↗
58. System wide MV distribution network technical losses estimation based on reference feeder and energy flow model10.1016/j.ijepes.2017.06.011 ↗
59. A new loss estimation method in limited data electric distribution network10.1109/TPWRD.2013.2273103 ↗
60. Grigoras, G., and Neagu, B.-C. (2019). Smart Meter Data-Based Three-Stage Algorithm to Calculate Power and Energy Losses in Low Voltage Distribution Networks. Energies, 12.10.20944/preprints201906.0200.v1 ↗
61. Soares, T.M., Bezerra, U.H., and Tostes, M.E.D.L. (2019). Full-Observable Three-Phase State Estimation Algorithm Applied to Electric Distribution Grids. Energies, 12.10.3390/en12071327 ↗
62. Kłosowski, Z., and Mazur, Ł. (2023). Influence of the Type of Receiver on Electrical Energy Losses in Power Grids. Energies, 16.10.3390/en16155660 ↗
63. Assessment of energy losses in power distribution systems with individual prosumers and energy communities10.1049/tje2.12243 ↗
64. Monitoring technical losses to improve non-technical losses estimation and detection in LV distribution systems10.1016/j.measurement.2020.107840 ↗
65. Jayasinghe, H., Gunawardane, K., and Nicholson, R. (2025). Applications of Electrical Load Modelling in Digital Twins of Power Systems. Energies, 18.10.3390/en18040775 ↗
66. Three-phase unbalance of distribution systems: Complementary analysis and experimental case study10.1016/j.ijepes.2010.12.003 ↗
67. Shen, Z., Zheng, Z., Fu, Y., and Yin, Z. (2023, January 25–27). The Impact of Three-phase Unbalance in Power Grid on the Losses of Distribution Equipment. Proceedings of the 2nd International Conference on Smart Grids and Energy Systems 2023, Guangzhou, China.10.1109/SGES59720.2023.10366892 ↗
68. Ochoa, L.F., Ciric, R.M., Padilha-Feltrin, A., and Harrison, G.P. (2005, January 22–26). Evaluation of distribution system losses due to load unbalance. Proceedings of the 15th Power Systems Computation Conference PSCC 2005, Liège, Belgium.
69. Research on Loss in Power Grid Induced by Three-Phase Imbalance
70. Jones, L., and Sumi, Z. (2023). Market Guide for Meter Data Management Systems, Gartner Research. Available online: https://www.gartner.com/en/documents/4023263.