2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 21 , Issue 2 , PP: 259-282, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Marwa M. Eid 1 *
Doi: https://doi.org/10.54216/FPA.210217
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.
Electricity Consumption Forecasting , Time-Series Prediction , Machine Learning , Greylag Goose Optimization , Energy Management
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