aspg-18-4132-1792478237 20261020063717 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 17 2 Zainab Hamed AlSidairi Universiti Tenaga Nasional (UNITEN), Kuala Lumpur, Malaysia Saraswathy Shamini Gunasekaran Universiti Tenaga Nasional (UNITEN), Kuala Lumpur, Malaysia 16.00 Normal false false false EN-US 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" QFormat="true" Name="heading 9"/> Name="index 1"/> 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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:115%; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Aptos",sans-serif; mso-ascii-font-family:Aptos; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Aptos; mso-hansi-theme-font:minor-latin; mso-font-kerning:1.0pt; mso-ligatures:standardcontextual;} The Accurate energy forecasting is vital for strategic planning, particularly in de-veloping economies with rapidly evolving demand patterns. This study pro-poses a hybrid Artificial Neural Network (ANN) model optimized using a modified JAYA algorithm to forecast energy consumption in Oman. The JAYA algorithm’s parameter-free, metaheuristic search improves ANN train-ing by enhancing convergence speed and reducing the risk of local minima. Historical data from 2017–2021—comprising GDP, population, and oil and gas production—were used as inputs. Model performance was benchmarked against an ANN trained with the Artificial Bee Colony (ABC) algorithm using mean square error (MSE), mean absolute error (MAE), relative error (RE), and root mean square error (RMSE) as evaluation metrics. Results show that ANN–JAYA consistently outperformed ANN–ABC, achieving lower error rates and greater robustness. The proposed approach offers a reliable deci-sion-support tool for policymakers and energy authorities, enabling more ef-fective resource allocation and long-term planning. Future research will ex-tend the framework to integrate renewable energy indicators and real-time data for adaptive, sustainable forecasting. 2025 250 259 4132 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.170216 https://www.americaspg.com/journal/18/article/4132 https://www.americaspg.com/storage/31758703383.pdf