Data-Driven Load Forecasting and Power Flow Optimization Using Deep LSTM Networks

In light of the rapidly increasing global demand for energy, accurately forecasting short-term electricity consumption has become a critical yet challenging task for modern power system operation and planning, as traditional methods often struggle to handle the variability and uncertainty of load demand. To address these limitations, this study proposes an integrated and data-driven framework that combines an Alternating Current Optimal Power Flow (ACOPF) model with a Long Short-Term Memory (LSTM) recurrent neural network in order to simultaneously enhance forecasting accuracy and operational efficiency. The LSTM model, trained on historical load data, is used to generate reliable 24-hour ahead electricity demand forecasts, which are then dynamically incorporated into the MATPOWER simulation environment using the IEEE 30-bus test system. The results demonstrate that the proposed approach achieves a high level of predictive performance, with a root mean square error (RMSE) of 0.3794, indicating its effectiveness in capturing temporal load patterns. More importantly, the integration of these forecasts into the ACOPF framework enables more proactive and informed decision-making in power system operations, leading to a significant improvement in economic dispatch by reducing the hourly generation cost from $576.89/h under conventional approaches to $490.91/h, corresponding to an approximate cost reduction of 14.9%. Overall, this study highlights the strong synergy between deep learning techniques and optimization models for smart grid management, showing that the proposed framework not only improves forecasting precision but also enhances system efficiency, reduces operational costs, and supports more reliable, flexible, and cost-effective power system operations.