Load Prediction

Load prediction focuses on accurately forecasting energy consumption or resource utilization across various systems, from power grids and charging stations to building energy management and computer networks. Current research emphasizes improving prediction accuracy and robustness using diverse machine learning models, including transformers, convolutional neural networks, recurrent neural networks (like LSTMs), and ensemble methods like XGBoost, often incorporating techniques like transfer learning and probabilistic forecasting to handle data scarcity and uncertainty. These advancements are crucial for optimizing resource allocation, enhancing grid stability, improving energy efficiency, and enabling proactive management of various systems.