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Machine learning-based residential load demand forecasting: Evaluating ELM, XGBoost, RF, and SVM for enhanced energy system and sustainability

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Evaluating ML methods like ELM and XGBoost enhances accuracy in predicting residential energy demands, suggesting improved planning.

ELM model achieved the highest R² of 0.971 in summer and 0.868 in winter, leading in accuracy among tested models.
The study employed techniques like ACF and CCF to process electricity consumption and temperature data for optimal model input.
Models were assessed based on R², mean absolute error, and root mean square error to evaluate their forecasting accuracy.
The findings indicate substantial improvements in load demand forecasting, aiding residential grid planning and management.

Abdalla et al. (2025) studied this question.

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