Abstract—This paper investigates electricity load forecasting using machine learning models enhanced with advanced optimization techniques. Six regression-based models—Gradient Boosting, LightGBM, ExtraTrees, Random Forest, Decision Tree, and Long Short-Term Memory (LSTM)—are evaluated on two real-world datasets from Panama City and Tetouan City, across hourly and 10-minute temporal resolutions. Results demonstrate that tree-based ensemble models, particularly the ExtraTreesRegressor, consistently outperform LSTM-based deep learning approaches. A key contribution is the development of an Enhanced Harris Hawks Optimization (EHHO) algorithm, incorporating adaptive parameter control and type-specific parameter handling. EHHO significantly improves hyperparameter tuning efficiency, enabling the ExtraTreesRegressor to achieve state-of-the-art forecasting accuracy. The EHHO-optimized ExtraTreesRegressor attains a Mean Absolute Percentage Error (MAPE) of 0.30% for Tetouan City and 1.47% for Panama City using 10-minute resolution data. The analysis reveals that higher temporal granularity contributes up to 65% improvement in forecasting performance compared to hourly data. These findings challenge the prevailing view of deep learning dominance in time-series forecasting and establish new accuracy benchmarks for electricity load prediction. The proposed methodology holds strong potential for practical deployment in grid operation, demand response, and renewable energy integration, supporting the development of more efficient and resilient energy systems.
 
Keywords—electricity load forecasting, machine learning, ensemble models, Harris Hawks optimization, smart grid


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