Load Forecasting

Load forecasting aims to accurately predict future electricity demand, crucial for efficient grid management and renewable energy integration. Current research emphasizes improving forecasting accuracy through advanced machine learning models, including deep learning architectures like Transformers, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs, such as LSTMs and GRUs), and hybrid approaches combining these techniques. These models often leverage diverse data sources, such as historical load data, weather information, and calendar effects, and are being enhanced with techniques like transfer learning, federated learning, and attention mechanisms to address data scarcity, privacy concerns, and model interpretability. Accurate load forecasting is vital for ensuring grid stability, optimizing energy resource allocation, and facilitating the transition to cleaner energy systems.