Accurate load forecasting is fundamental to grid optimization. Modern deep learning approaches have reduced forecasting errors by up to 30% compared to traditional methods.

The most effective neural network architectures for load forecasting combine LSTM networks to capture temporal patterns, CNNs to detect local patterns, and attention mechanisms to weight the importance of different inputs.