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This is a Literature Review study (also classified as: Systematic Review).

August 17, 2025Open Access

Energy Load Forecasting with Machine Learning: Models, Metrics, and Future Directions

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This review demonstrates the effectiveness of machine learning models in energy load forecasting, highlighting challenges in smart grid applications.

Energy load forecasting significantly improves the management and operation of smart grids, enhancing energy distribution efficiency.
The review evaluates various machine learning models such as LSTM and GRUs, showing their strengths for different forecasting timeframes.
Challenges like data quality and model interpretability remain significant, necessitating better integration of external factors.
Emerging trends like hybrid models and reinforcement learning could offer solutions to enhance forecasting systems in smart grids.

Muhammad Faraz Manzoor (2025) studied this question.

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August 17, 2025Open Access

View Full Paper

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Authors

This review demonstrates the effectiveness of machine learning models in energy load forecasting, highlighting challenges in smart grid applications.

Energy load forecasting significantly improves the management and operation of smart grids, enhancing energy distribution efficiency.
The review evaluates various machine learning models such as LSTM and GRUs, showing their strengths for different forecasting timeframes.
Challenges like data quality and model interpretability remain significant, necessitating better integration of external factors.
Emerging trends like hybrid models and reinforcement learning could offer solutions to enhance forecasting systems in smart grids.

Muhammad Faraz Manzoor (2025) studied this question.

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