Intelligent Dispatching Method of Short-term Load in Distributed Power System Using Deep Learning

In traditional short-term load dispatching methods for distributed power systems, complex data processing, and low recognition accuracy hinder effective system optimization.To address this issue, an intelligent short-term load dispatching method for distributed power systems based on deep learning was proposed, aiming to improve voltage quality and reduce network losses.The proposed method first collected historical load data from the system, including active power and reactive power, and utilized a long short-term memory neural network (LSTM) for short-term load forecasting.In the forecasting process, the input gate, output gate, and forget gate operations were employed to accurately handle load variations.Based on the forecasting results, a short-term load dispatching model was constructed to minimize network losses, voltage deviations, and power abandonment rates, while incorporating constraints such as power flow, node voltage, and load regulation coefficients.The bee colony Quantum-behaved Particle Swarm Optimization (QPSO) algorithm was used to achieve intelligent load allocation in the distributed power system.Experimental results show that, the proposed method effectively controls voltage fluctuations within 0.4 p.u. while reducing network losses to below 0.14 MW, significantly improving the overall system performance.The conclusion provides scientific technical supports for the optimization and dispatching of distributed power systems and validates the applicability and effectiveness of intelligent methods in complex power system scenarios.