aspg-3-4099-1791499249 20261008224049 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2026 21 2 Marwa M. Eid Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, Egypt; Jadara Research Center, Jadara University, Irbid 21110, Jordan 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"/> Name="index 2"/> Name="index 3"/> Name="index 4"/> Name="index 5"/> Name="index 6"/> Name="index 7"/> Name="index 8"/> Name="index 9"/> UnhideWhenUsed="true" Name="toc 1"/> UnhideWhenUsed="true" Name="toc 2"/> UnhideWhenUsed="true" 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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;} This paper addresses the challenge of predicting and analyzing electricity consumption patterns in Tetouan, Morocco, using time-series data. The dataset consists of 52,416 observations with 9 features, collected from the SCADA system of electricity consumption across three zones. The primary goal is to enhance forecasting accuracy and optimize prediction models through machine learning (ML) algorithms, including both timeseries models and advanced optimization techniques. We compare the performance of several baseline ML models, such as BiLSTM and Continuous Time Stochastic Modelling (CTSM), with their optimized versions, utilizing optimization algorithms like Greylag Goose Optimization (GGO), Bat Algorithm (BA), and Whale Optimization Algorithm (WOA). The results show that the optimized CTSM model, using GGO, achieved substantial improvements, including the lowest Mean Squared Error (MSE) of 7.09E-07 and the highest R² of 0.990, demonstrating superior accuracy and stability. The contributions of this work include (i) benchmarking various ML models for time-series forecasting, (ii) introducing the use of optimized CTSM with meta-heuristics, and (iii) evaluating model performance using a comprehensive set of statistical metrics. 2026 259 282 4099 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.210217 https://www.americaspg.com/journal/3/article/4099 https://www.americaspg.com/storage/21758035278.pdf