Simulation Study on Optimizing Neural Network in Short-Term Electric Load Prediction

It researches the short-term electric load prediction and short-term electric load has the characteristics of time-varying, uncertainty, nonlinearity, etc., so the traditional linear prediction method cannot correctly describe the changing rule of the short-term electric load prediction, and neural network has the deficiencies including local minimum value of neural network, over-fitting and weak generalization ability, and the prediction accuracy is lower. In order to improve the accuracy of the short-term electric load prediction, this paper proposes a short-term electric load prediction model (IQPSOBPNN) based on optimizing BP neural network. Firstly, it improves Quantum Particle Swarm Optimization to optimize the BP neural network parameters, and then adopts the optimized BP neural network to conduct modeling for the nonlinear change law of the short-term electric load prediction. Finally, it takes simulation test for the model performance. The simulation result shows that IPQPSO solves the problems of the BP neural network, and improve the prediction accuracy of the short-term electric load and reduce the prediction error.