Research on Deep Learning-Based Dynamic Load Forecasting and Optimal Dispatch in Smart Grids

Abstract

The integration of deep learning into smart grid operations addresses critical challenges in dynamic load forecasting and optimal dispatch amid increasing renewable energy penetration. This study proposes a hybrid LSTM-Transformer architecture for multi-scale temporal-spatial load prediction, achieving 28% RMSE reduction on real-world datasets (CAISO, PJM), coupled with a deep reinforcement learning framework for multi-objective dispatch optimization that lowers operational costs by 12.4% while ensuring stability constraints. The synergy between adaptive forecasting models and scenario-based stochastic optimization demonstrates superior performance in handling renewable intermittency and demand volatility, validated through grid-scale case studies. Methodological innovations in federated feature extraction and carbon-aware scheduling further enhance scalability for distributed energy systems. These advancements provide actionable insights for grid operators transitioning to low-carbon paradigms, emphasizing computational efficiency and interoperability with legacy infrastructure.