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

Energy load forecasting plays a crucial role in the efficient management and operation of smart grids, enabling utilities to optimize energy distribution and improve grid reliability. Recent advancements in machine learning (ML) techniques have significantly enhanced the accuracy and adaptability of energy load prediction models. This review explores various ML models used for energy load forecasting, including traditional models, deep learning approaches such as long short-term memory (LSTM) networks and gated recurrent units (GRUs), and ensemble methods. The review discusses the strengths and limitations of each approach, highlighting its applicability to different forecasting timeframes and data characteristics. Additionally, it examines the performance evaluation metrics commonly used to assess model accuracy and reliability. Although multiple studies have examined forecasting techniques, there is a lack of comprehensive evaluation that connects model choice with practical deployment constraints such as data quality, real-time scalability, and interpretability. This review addresses this gap by systematically analyzing the challenges and emerging solutions in the context of smart grid applications. Despite the progress made, challenges related to data quality, computational complexity, and model interpretability remain significant barriers. The review concludes with an exploration of emerging trends and future directions in energy load forecasting, including hybrid models, federated learning, and reinforcement learning, which offer promising solutions to overcome existing limitations and improve forecasting performance in smart grid systems. Overall, the findings suggest that while no single model is universally optimal, integrating external factors, improving data quality, and adopting hybrid or explainable artificial intelligence (AI) approaches are critical for building more accurate, scalable, and interpretable forecasting systems.