<![CDATA[The growth of electrical energy consumption continues to increase, particularly during peak-load periods, posing significant challenges to power system reliability. Inaccurate peak-load forecasting can lead to supply–demand imbalances, reduced power plant operational efficiency, and an increased risk of power outages. This study aims to develop a machine learning-based peak-load forecasting model by integrating a Multilayer Perceptron (MLP) with the Grey Wolf Optimizer (GWO) algorithm to achieve more accurate and stable forecasting performance. The dataset consists of historical electricity load data collected from January 2024 to January 2025 and is supplemented with relevant weather variables. The proposed model is evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results demonstrate that the integrated MLP–GWO model achieves improved forecasting performance compared with the standard MLP, attaining a MAPE of 0.69%. The findings indicate that GWO-based optimization improves convergence and model stability while enhancing the ability of the MLP to capture nonlinear patterns in daily peak-load demand. Therefore, the proposed MLP–GWO model provides an effective approach for peak-load forecasting and can support power system planning, including generation capacity assessment, distribution network reinforcement, and renewable energy integration, thereby contributing to a more reliable and sustainable electricity system.]]>
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