aspg-18-3541-1791791945 20261012075905 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2025 15 2 Huda W. Ahmed Informatics Institute for Postgraduate Studies, University of Information Technology and Communications, Baghdad, Iraq Asma Khazaal Abdulsahib University of Baghdad, Collage of education for human science, Ibn Rused, Baghdad, Iraq Massila Kamalrudin Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Malaysia Mustafa Musa Center of Research and Innovation Management, Universiti Teknikal Malaysia Melaka, Malaysia Normal false false false TR X-NONE AR-SA DefSemiHidden="false" DefQFormat="false" DefPriority="99" LatentStyleCount="371"> UnhideWhenUsed="true" QFormat="true" Name="heading 2"/> UnhideWhenUsed="true" QFormat="true" Name="heading 3"/> UnhideWhenUsed="true" QFormat="true" Name="heading 4"/> UnhideWhenUsed="true" QFormat="true" Name="heading 5"/> UnhideWhenUsed="true" QFormat="true" Name="heading 6"/> UnhideWhenUsed="true" QFormat="true" Name="heading 7"/> UnhideWhenUsed="true" QFormat="true" Name="heading 8"/> UnhideWhenUsed="true" 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Name="Grid Table 7 Colorful Accent 3"/> Name="Grid Table 1 Light Accent 4"/> Name="Grid Table 6 Colorful Accent 4"/> Name="Grid Table 7 Colorful Accent 4"/> Name="Grid Table 1 Light Accent 5"/> Name="Grid Table 6 Colorful Accent 5"/> Name="Grid Table 7 Colorful Accent 5"/> Name="Grid Table 1 Light Accent 6"/> Name="Grid Table 6 Colorful Accent 6"/> Name="Grid Table 7 Colorful Accent 6"/> Name="List Table 1 Light Accent 1"/> Name="List Table 6 Colorful Accent 1"/> Name="List Table 7 Colorful Accent 1"/> Name="List Table 1 Light Accent 2"/> Name="List Table 6 Colorful Accent 2"/> Name="List Table 7 Colorful Accent 2"/> Name="List Table 1 Light Accent 3"/> Name="List Table 6 Colorful Accent 3"/> Name="List Table 7 Colorful Accent 3"/> Name="List Table 1 Light Accent 4"/> Name="List Table 6 Colorful Accent 4"/> Name="List Table 7 Colorful Accent 4"/> Name="List Table 1 Light Accent 5"/> Name="List Table 6 Colorful Accent 5"/> Name="List Table 7 Colorful Accent 5"/> Name="List Table 1 Light Accent 6"/> Name="List Table 6 Colorful Accent 6"/> Name="List Table 7 Colorful Accent 6"/> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0in; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi; mso-ansi-language:TR;} Accurate energy metering is essential for reliable power system operation, fair billing, and effective monitoring of electricity consumption. However, detecting faults in electric energy meters remains challenging because conventional inspection practices, including manual testing, operational sampling, and user-reported verification, are time-consuming, labor-intensive, and often limited in dynamic field conditions. This study proposes a deep learning-assisted prediction model (DLPM) for identifying abnormal metering behavior and improving the assessment of energy meter faults in electric power systems. The proposed model learns the relationship between expected and observed meter trajectories, enabling it to detect significant deviations that may indicate measurement errors or operational faults. By automating the analysis of metering discrepancies, the DLPM provides a more consistent and data-driven alternative to traditional fault diagnosis methods. The model supports accurate deviation estimation, improves abnormality recognition, and assists in identifying potential causes of smart meter malfunction. Simulation results demonstrate that the proposed DLPM achieves strong predictive performance, with 99.2% accuracy, 97.8% overall performance, and 98.9% efficiency. In addition, the model records an average consumption deviation of 10.3% and a root mean square error of 11.2%, indicating its effectiveness in supporting intelligent meter fault assessment. These findings suggest that deep learning can enhance the reliability, automation, and diagnostic capability of smart metering systems in modern electric power networks. 2025 91 103 3541 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.150207 https://www.americaspg.com/journal/18/article/3541 https://www.americaspg.com/storage/31778068017.pdf